[Thesis Proposal] Predictive Exploration for Autonomous Science

Speaker: David Thompson

Abstract:
Planetary science is entering a new era in which exploration robots can outrun their own ability to collect science data. Autonomous navigation will soon permit single-command traverses of multiple kilometers. Nevertheless, the time for taking measurements and the bandwidth available for transmitting them to Earth will remain relatively constant. A growing body of research addresses these bottlenecks with onboard data understanding. Autonomous rovers can use pattern recognition, learning and planning technologies to place instruments and take measurements without human supervision. These robots autonomously choose the most important features to observe and transmit, traveling longer distances without sacrificing our understanding of the visited terrain.

I argue that intelligent explorer agents must exploit structure in their environment. In other words, they must be mapmakers. Maps can represent spatial structure (similarities from one locale to the next) and inter-sensor structure (correlations between different sensing modes). “Predictive exploration” formulates mapmaking as an experimental design problem. Generative spatial models guide the agent to informative areas while minimizing redundant measurements. Information gain over the map determines exploration decisions, while a similar criterion suggests the best data products for downlink. We will demonstrate these principles with a rover system that autonomously builds kilometer-scale geologic maps.

A copy of the thesis proposal document:
http://www.cs.cmu.edu/~drt/ThompsonProposal.pdf.

News: European Researchers Developing 'Emotional Robot'

The link. February 24, 2007 2:35 p.m. EST

Som Patidar - All Headline News Staff Writer

London, Britain (AHN) - A joint research project by a European team-led by British researchers is developing a robot that can interact with people emotionally.

The research project, Feelix Growing, involves six countries and 25 roboticists, developmental psychologists and neuroscientists.

The project's coordinator Lola Canamero, from Britain's University of Hertfordshire, said that the aim is to develop robots that grow up and adapt to humans in everyday environments.

"If robots are to be truly integrated in humans everyday lives as companions or careers, they cannot be just taken off the shelf and put into a real-life setting, they need to adapt to their environment," Canamero said.

News: SheekGeek educational kits

The W.A.S.P Original Robotic Kit is designed to help introduce children age 12 and up to robotics, electronics, and mechanics.

The name W.A.S.P stands for "Wiggling and Spinning Photovore" which describes the action and the type of the robot in the W.A.S.P. robot kit. A photovore is a light-seeking robot. The W.A.S.P photovore robot wiggles towards a light source and spins in circles when it finds the brightest spot.

The activity of the W.A.S.P is very reactive. It will follow the beam of a flashlight closely allowing the user to control where they want the W.A.S.P to go. The W.A.S.P is also very quick (especially with new batteries). It can travel 5 feet in 5 seconds!

Setup of the W.A.S.P is great for beginners. The circuit is very simple and many of the pieces are everyday, recognizable items like chenille stems/pipe cleaners and cable ties.

SheekGeek Educational Kits - Link
SheekGeek Original W.A.S.P. Robotic Kit - Link
The W.A.S.P. Original Robotic Kit Contents - Link

Call For Papers: Special Issue on Network Robot Systems (Robotics and Autonomous System Journal)

**************************************************
SPECIAL ISSUE ON NETWORK ROBOT SYSTEMS (NRS)
(ROBOTICS AND AUTONOUMOUS SYSTEMS JOURNAL)
**************************************************

The last decade has witnessed unprecedented interaction between technological developments in computing and communications, which have led to the design and implementation of robotic and automation systems consisting of networked vehicles, sensors and actuators systems. These developments enable researchers and engineers not only to design new robotics systems but also to develop systems that could have not been imagined before. Now, there is a need for a unifying paradigm within the robotics community to address the design of these networked automation systems.

The name Networked Robots (NR) was created in May 2004 within the IEEE RAS Technical Committee, as a consequence of the preliminary work on Internet-based tele-operated robots initiated in 2001, and its expansion to reflect a broader set of problems and applications. There are several definitions of NRS, coming from US and Japan, but a simple and comprehensive definition of NRS is:

“A Network Robot System is a group of artificial autonomous systems that are mobile and that make important use of wireless communications among them or with the environment and living systems in order to fulfil their tasks”.

Network Robot Systems (NRS) call for the integration of several fields: robotics, perception (sensor systems), ubiquitous computing, and network communications. Some of the key issues that must be addressed in the design of Network Robot Systems are cooperative localization and navigation, cooperative environment perception, cooperative map building, cooperative planning and planning for cooperation, human-robot interaction, network tele-operation, and communications.

The topic Network Robot Systems transcends “conventional” robotics, in the sense that there exists for these type of distributed heterogeneous systems, an interrelation among a community of robots, environment sensors and humans. Applications include network robot teams (for example for space applications), human-robot networked teams (for example a community of robots that assist people), robots networked with the environment (for example for tasks on urban settings or rescue) or geminoid robots (a replication of a human with own autonomy and being partially tele-operated through the network).

The topics of interest include, but are not limited to:
- human robot symbiosis
- networked environing sensing/actuation
- distributed environing system
- interaction between human and environing components
- networked human-robot interaction
- coordination and cooperation among multiple types of robots
- self-configuration of a network robot system
- monitoring and self-repair of a network robot system
- network robot platform
- security for network robot systems
- socially situated network robots
- applications of network robot systems

IMPORTANT DATES
First Call for papers: 15 February 2007
Paper submission deadline: 15 May 2007
Revised notification: 10 September 2007
Final paper submission: 22 October 2007
Final decision notification: 16 November 2007

REVIEWING PROCESS
Expected contributions should be around 12 pages long. Submissions have to be sent to the Guest Editors (sanfeliu@iri.upc.es; hagita@atr.jp, asaffio@aass.oru.se) in electronic form (PDF files). The Guest Editors will first evaluate all manuscripts. Manuscripts meeting the minimum criteria are passed on for peer review, to be accomplished by two external experts. The method of review in this special issue will employ single blind review, where the referee remains anonymous throughout the process. The Guest Editors board is responsible for the final decision to accept
or reject the articles, based on the recommendations of the reviewers. Accepted papers will have to be sent to the Guest Editors electronically, both as source files (LaTex, MS Word, including all original Figures/Tables and References) and in printable version (PDF). Please follows the instructions in http://ees.elsevier.com/robot/.

GUEST EDITORS
• Alberto Sanfeliú, Technical University of Catalonia, Spain, sanfeliu@iri.ups.es
• Norihiro Hagita, ATR Intelligent Robotics and Communication Laboratories, Japan, hagita@atr.jp
• Alessandro Saffiotti, Örebro University, Sweden, asaffio@aass.oru.se

RELATED LINKS:
- Research Atelier on Network Robot Systems, http://turina.upc.es/nrs
- Japan Network Robot Forum, http://www.scat.or.jp/nrf/English/
- IEEE RAS Technical Committee, http://www.informatik.uni-freiburg.de/~burgard/tc/

Non-rigid point set registration : Coherent Point Drift

Author :

Andriy Myronenko
Xubo Song
Miguel A´ . Carreira-Perpin˜a´n

OGI School of Science and Engineering
Oregon Health and Science University

Title :

Non-rigid point set registration : Coherent Point Drift

Abstract :

We introduce Coherent Point Drift (CPD), a novel probabilistic method for nonrigid registration of point sets. The registration is treated as a Maximum Likelihood (ML) estimation problem with motion coherence constraint over the velocity field such that one point set moves coherently to align with the second set. We formulate the motion coherence constraint and derive a solution of regularized ML estimation through the variational approach, which leads to an elegant kernel form. We also derive the EM algorithm for the penalized ML optimization with deterministic annealing. The CPD method simultaneously finds both the non-rigid transformation and the correspondence between two point sets without making any prior assumption of the transformation model except that of motion coherence. This method can estimate complex non-linear non-rigid transformations, and is shown to be accurate on 2D and 3D examples and robust in the presence of outliers and missing points.

Link :
paper
project page

Patent: Underground GPS

13:48 12 February 2007
NewScientist.com news service
Barry Fox

Underground GPS

Satellite navigation is becoming a vital tool for the modern motorist. But GPS (Global Positioning System) receivers need to compare signals from at least three orbiting satellites to determine their position. This means satellite navigation does not normally work inside a tunnel, underground or in a heavily built up area.
Two inventors from Cambridge in the UK are now patenting a system that could let satellite equipment acquire positioning information even when satellite signals are blocked. The roof of the building, or the ground above the tunnel, is fitted with at least four directional antennae focused on different patches of the sky. These antennae receive GPS signals, then amplify and re-broadcast them using transmitters positioned at specific points below ground. A GPS device is then fooled into behaving as if it were out in the open, providing accurate positional data from inside a tunnel, in an underground car park or in a heavily built up city. The same trick could also let GPS devices work inside buildings.

See the patent application.

Spatial Reasoning: Planning Among Movable Obstacles

Author:
Mike Stilman
Robotics Institute
Carnegie Mellon University

Abstract:
Autonomous robots operating in real world, unstructured environments cannot rely on the existence of collision free paths or feasible trajectories. Search and rescue, construction and planetary exploration domains contain debris that obstructs the robots path. Theoretically, one can represent all possible interactions between the robot and these objects as a single search problem. However, the resulting nonlinear state space would be exponentially large. In this thesis we explore methods for reasoning about the robots state space to reduce problem dimensionality and accomplish autonomous motion in the presence of movable objects.

Further Details:
A copy of the thesis proposal document can be found at http://www.cs.cmu.edu/~mstilman/proposal/stilman-proposal.pdf.

CMU Intelligence Seminar: Bayesian models of human learning and inference

Bayesian models of human learning and inference
Josh Tennenbaum, MIT

Faculty Host: Tom Mitchell

Bayesian methods have revolutionized major areas of artificial intelligence, machine learning, natural language processing and computer vision. Recently Bayesian approaches have also begun to take hold in cognitive science, as a principled framework for explaining how humans might learn, reason, perceive and communicate about their world. This talk will sketch some of the challenges and prospects for Bayesian models in cognitive science, and also draw some lessons for bringing probabilistic approaches to artificial intelligence closer to human-level abilities.

The focus will be on learning and reasoning tasks where people routinely make successful generalizations from very sparse evidence. These tasks include word learning and semantic interpretation, inference about unobserved properties of objects and relations between objects, reasoning about the goals of other agents, and causal learning and inference. These inferences can be modeled as Bayesian computations operating over constrained representations of world structure -- what cognitive scientists have called "intuitive theories" or "schemas". For each task, we will consider how the appropriate knowledge representations are structured, how these representations guide Bayesian learning and reasoning, and how these representations could themselves be learned via Bayesian methods. Models will be evaluated both in terms of how well they capture quantitative or qualitative patterns of human behavior, and their ability to solve analogous real-world problems of learning and inference. The models we discuss will draw on -- and hopefully, offer new insights for -- several directions in contemporary machine learning, such as semi-supervised learning, modeling relational data, structure learning in graphical models, hierarchical Bayesian modeling, and Bayesian nonparametrics.

Speaker Bio
Josh Tenenbaum studies learning and reasoning in humans and machines, with the twin goals of understanding human intelligence in computational terms and bringing artificial intelligence closer to human-level capacities. He received his Ph.D. from MIT in 1999, and from 1999-2002, he was a member of the Stanford University faculty in the Departments of Psychology and (by courtesy) Computer Science. In 2002, he returned to MIT, where he currently holds the Paul E. Newton Career Development Chair in the Department of Brain and Cognitive Sciences, and is a member of the Computer Science and Artificial Intelligence Laboratory. He has published extensively in cognitive science, machine learning and other AI fields, and his group has received several outstanding paper or student-paper awards at NIPS, CVPR, and Cognitive Science. He received the 2006 New Investigator Award from the Society for Mathematical Psychology, and the 2007 Young Investigator Award from the Society of Experimental Psychologists. He serves as an associate editor of the journal Cognitive Science and is currently co-organizing a summer school on "Probabilistic Models of Cognition: The Mathematics of Mind" for July 2007 at IPAM, the Institute of Pure and Applied Mathematics at UCLA.

Simulating Thought to Model Terrorists

A rock star among game developers, Silverman and his team of 20 researchers and graduate students at the University of Pennsylvania's Ackoff Center for Advancement of Systems Approaches have gone well beyond any video game in existence. They imbue agents with detailed physiologies that respond to hunger, fatigue, and stress, as well as minds that encompass complex reasoning skills, value systems, and up to 22 emotions. This is the closest a computer comes to simulating a real person, and is at the cutting edge of computational behavior modeling.

[LINK]

CMU RI Thesis Proposal: Integrated Localization, Mapping, and Planning in Unstructured 3D Environments

Nathaniel Fairfield (than@cmu.edu)
Robotics Institute
Carnegie Mellon University

Abstract:

The ability to explore an unknown environment is a prerequisite for most useful mobile robotics. Exploration can be decomposed into the tasks of perceiving the environment to build a map, localizing within that map, and planning where to explore next. Over the past ten years or so, the field of simultaneous localization and mapping (SLAM) has been active and increasingly applied. More recently, work has been directed towards the problem of planning as an integral part of exploration and SLAM. Another persistent challenge is scale many SLAM formulations have problems with exploring areas. We are interested in developing an integrated mapping, localization, and planning approach that can handle large scale three-dimensional environments and sparse sensor data. As a start, we have developed a method for doing SLAM using a Rao-Blackwellized Particle Filter and evidence grid-based maps, and demonstrated successful SLAM using an autonomous underwater vehicle in a 3D environment. The two major limitations of our current method are its inability to scale the evidence grid approach to truly large environments (hundreds of meters and millions of observations), and its lack of planning ability for picking exploration and/or uncertainty-reducing actions. We propose to address the first limitation by developing SLAM on multiple scales: local submaps and global maps; in effect using the submaps as features at larger scale. We propose to address the second limitation, planning, by integrating the tasks of mapping, localizing, and planning under an information-theoretic framework. The planning algorithm will use models of unmapped regions and the entropy of the predicted SLAM state to choose the action with the greatest estimated information gain. The combination of multi-scale SLAM and information gain-based planning raises the possibility of hierarchical exploration, where the robot's current task determines its exploration strategy. In this proposal we describe our current work, motivation, and proposed solutions, with the goal of building a system which is capable of exploring large-scale 3D environments.

Further Details: http://gs4435.sp.cs.cmu.edu/fairfield_proposal.pdf

ICRA07: Identification and Control of an Autonomous Blimp

Gaussian Processes and Reinforcement Learning for Identification and Control of an Autonomous Blimp

Abstract:

Blimps are a promising platform for aerial robotics and have been studied extensively for this purpose. Unlike other aerial vehicles, blimps are relatively safe and also possess the ability to loiter for long periods. These advantages, however, have been difficult to exploit because blimp dynamics are complex and inherently non-linear. The classical approach to system modeling represents the system as an ordinary differential equation (ODE) based on Newtonian principles. A more recent modeling approach is based on representing state transitions as a Gaussian process (GP). In this paper, we present a general technique for system identification that combines these two modeling approaches into a single formulation. This is done by training a Gaussian process on the residual between the non-linear model and ground truth training data. The result is a GP-enhanced model that provides an estimate of uncertainty in addition to giving better state predictions than either ODE or GP alone. We show how the GP-enhanced model can be used in conjunction with reinforcement learning to generate a blimp controller that is superior to those learned with ODE or GP models alone.

Original link:
http://www.cs.washington.edu/homes/fox/abstracts/gp-blimp-icra-07.abstract.html

Paper link:
http://www.cs.washington.edu/homes/fox/postscripts/gp-blimp-icra-07.pdf

Stanford Talk: Large Scale Detection of Irregularities in Accounting Data

Large Scale Detection of Irregularities in Accounting Data

Stephen Bay, Center for Advanced Research, PricewaterhouseCoopers LLP

Abstract:
In recent years, there have been several large accounting frauds where a company's financial results have been intentionally misrepresented by billions of dollars. In response, regulatory bodies have mandated that auditors perform analytics on detailed financial data with the intent of discovering such misstatements. For a large auditing firm, this may mean analyzing millions of records from thousands of clients. In this talk, I will discuss techniques for automatic analysis of company general ledgers on such a large scale to identify irregularities -- which may indicate fraud or just honest errors -- for additional review by auditors. These techniques have been implemented in a prototype system, called Sherlock, which combines aspects of both outlier detection and classification. In developing Sherlock, we faced three major challenges: developing an efficient process for obtaining data from many heterogeneous sources, training classifiers with only positive and unlabeled examples, and presenting information to auditors in an easily interpretable manner.

MIT CSAIL talk: Neural Discrimination of Complex Natural Sounds in Songbirds

Title: Neural Discrimination of Complex Natural Sounds in Songbirds
Speaker: Dr. Kamal Sen , Neural Coding Laboratory, Hearing Research Center, Boston University
Date: Wednesday, February 14 2007

Discrimination and recognition of complex natural stimuli is a fundamental problem that arises in a wide variety of fields e.g., neuroscience and computer science. In neuroscience an important problem is to understand how animals and humans discriminate between complex sounds e.g., vocal communication sounds of two different individuals. In computer science, speech recognition algorithms must solve a similar problem. Moreover, such discrimination must often be performed in noisy environments, e.g., a cocktail party. How does the brain solve this problem? Currently, relatively little is known about the neural basis for complex sound discrimination and recognition. An attractive model system for investigating this question is the songbird, which shows striking analogies to humans in the context of speech. In this talk, I will describe our ongoing work on the neural discrimination of birdsongs in field L, the analog of primary auditory cortex, in zebra finches. I will present some of our findings on the accuracy and time-scales of neural discrimination, sensitivity vs. invariance to stimulus parameters e.g., intensity, and then discuss how we are extending this paradigm to investigate more complex auditory scenes, e.g., a cocktail party.

CMU VASC seminar: Computer Vision in Archaeology: Recent Case Studies

Computer Vision in Archaeology: Recent Case Studies
Kevin Cain
Institute for Study and Integration of Graphical Heritage Techniques

Computing for archaeology is a study in contrasts: graphics and vision techniques are still somewhat exotic, but interesting (and difficult) problems abound! In this talk, we'll present a snapshot of current needs in archaeology, framing the discussion with results from the past seven seasons of field work at the memorial temple of Ramses II in Egypt. Topics include: 3d representations of ancient sites, large scale orthomosaics of inscribed wall surfaces, lighting capture, relighting, and site reconstructions. We'll also take a look at efforts to present archaeological results in novel environments, including a new NSF 'full dome' film project Maya Skies and a large digital projection installation in Egypt's Valley of the Kings.

CMU VASC seminar: Observations from Parsing Images of Architectural Scenes

Observations from Parsing Images of Architectural Scenes
Alexander Berg
UC Berkeley

Computational models for visual recognition show promise for some tasks. I will review our success in this area and show some information theoretic comparisons with our ongoing work on parsing scenes. For images of architectural scenes we have observed that very simple independent local features provide a great deal of information about what components -- building, sky, ground, etc. -- make up a scene. In addition a few carefully chosen image wide latent variables are added to the model then even more information is available. Finally given this coarse level parsing it is possible to effectively identify features such as windows and roof-lines that would be difficult to parse in isolation.

CMU ML lunch: Discrete Markov Random Fields -- the Inference story

Discrete Markov Random Fields -- the Inference story

Speaker: Pradeep Ravikumar, CMU
http://www.cs.cmu.edu/~pradeepr

Abstract: Markov random fields, or undirected graphical models, are graphical representations of probability distributions. Each graph represents a family of distributions -- the nodes of the graph represent random variables, the edges encode independence assumptions, and weights over the edges and cliques specify a particular member of the family.

In this talk, I will give the high-level intuitions behind the wide array of inference techniques for discrete markov random fields.

The problem of inference in markov random fields is, in generality, the problem of answering queries about the probability distribution represented by the markov random field. Key inference tasks include partition function estimation, event probability estimation, and computing the most probable configuration. The talk will give a high-level picture of these queries, and the methods used to answer these queries.

Invention: On-road warning signs

On-road warning signs

* 16:27 05 February 2007
* NewScientist.com news service
* Barry Fox

Could real-time traffic information be projected directly onto the road ahead?

Philips thinks so and proposes attaching laser projectors, each with a rapidly-moving mirror that deflects its beam, to ordinary lampposts. These would be used to project images and words onto the road just ahead of approaching cars.

The solution would be cheaper than installing a large video display and safer too, since drivers would not need to take their eyes off the road. Also, a warning about ice or danger on the road ahead would not need a full colour screen, so the projector could use just a single-colour laser.

Each lamppost would have its own IP address and would connect wirelessly, or via a cable, to a central traffic control centre. The projectors could also tap into the power already used to illuminate streetlamps.

As well as providing warning signs, the laser projectors could paint temporary lanes onto the road, steering traffic round an obstruction, or away from the main highway and onto a side road. It's a neat idea, but how well would it work in busy traffic?

Read the full on-road warning signs patent application.

News: (Invention) Covert iris scanner

Invention: Covert iris scanner

* 16:27 05 February 2007
* NewScientist.com news service
* Barry Fox

Covert iris scanner

Sarnoff Labs in New Jersey, US, has been working on a clever homeland security system for the US government. It scans people's irises as they walk towards a checkpoint, without them even knowing it.

Current systems require a person to stand still and look directly into a single digital camera from close range. The new system will instead use an array of compact, high resolution cameras, all of which point in slightly different directions and focus at slightly different distances.

As a subject walks into range, a sensor triggers a powerful infrared strobe light. The strobing is synchronised with the camera exposures, illuminating pictures of a subject's face thirty times per second, to create a bank of different images.

At least one of these shots should provide a clear, high-definition image of the target's iris. Clarity could also be enhanced by combining two similar shots. Sarnoff reckons this could be done at a distance around 3 metres, and a database could be queried fast enough to sound the alarm if the subject warrants a closer check. Let's just hope the target is not wearing sunglasses.

the full covert iris scanner patent application.

CMU Intelligence Seminar: Adaptation, Inference, and Optimization: Speech Driven Machine Learning

Adaptation, Inference, and Optimization: Speech Driven Machine Learning
Jeff Bilmes
University of Washington

Speech applications (such as speech recognition) have a long history of utilizing statistical learning methodology. In this talk, we will describe how machine learning research can be motivated by the application of speech processing, including speech recognition and speech-based human-computer interaction. First, dynamic graphical models (e.g., DBNs, and CRFS) can be used to express many novel speech recognition procedures. We describe new methods to perform fast exact and approximate inference in such models. These methods involve, as expected, graph triangulation, conditioning, and search, but perhaps more surprisingly, also involve optimal bin packing, max-flow procedures, and submodular matchings. In the second part of the talk, we will describe a new speech application, the Vocal Joystick, for specifying multi-dimensional continuous control parameters using non-verbal vocalizations. This application has resulted in sample complexity bounds for model adaptation, and adaptation strategies for discriminative classifiers (SVMs and Neural Networks).


Speaker Bio.: Jeff A. Bilmes is an Associate Professor in the Department of Electrical Engineering at the University of Washington, Seattle (adjunct in Linguistics and in Computer Science and Engineering). He co-founded the Signal, Speech, and Language Interpretation Laboratory at the University. He received a masters degree from MIT, and a Ph.D. in Computer Science at the University of California, Berkeley. Jeff is the main author and designer of the graphical model toolkit (GMTK), and has done much research on both structure learning of and fast probabilistic inference in dynamic Graphical models. His main research lies in statistical graphical models, speech, language and time series, machine learning, human-computer interaction, combinatorial optimization, and high-performance computing. He was a general co-chair for IEEE ASRU 2003, and HLT/NAACL 2006. He is a member of the IEEE, ACM, and ACL, is a 2001 CRA Digital-Government Research Fellow, and is a 2001 recipient of the NSF CAREER award.

IEEE news: MOVIES IN THE ROUND (Cool Stuff!)

Most of the so-called 3-D displays you've seen use stereoscopic tricks to create the feeling of depth, but it's just an illusion. Move your head, and you won't see round any corners. Now comes Holografika, a Budapest firm, that gives the real deal: a flat-panel display that exploits the principle holography to present movies that look different to people standing at different points. The link.

IEEE news: THOUGHT POWERED WHEELCHAIRS IN DEVELOPMENT

A new wheelchair commanded by thoughts is currently in development by researchers at the University of Zaragoza in Spain. The chair will use a process called brain-computer interface, or BCI for short, which involves attaching electroencephalogram electrodes to a rider’s scalp, which then record brain rhythms and convey them to the chair’s computer. Two 800-MHz Intel computers mounted on the wheelchair will process these readings and send instructions to the wheels. While the signals are crude, advances in decoding algorithms are have made it possible to train the software to recognize simple commands such as turn left or turn right. Over time, more specific commands, such as moving to a certain room by thinking of it, will become understood by the software. To combat misinterpreted commands, a laser will be attached to the front of the chair to avoid collisions with the surroundings. The technology is still a couple years away from being perfected, and the researchers are looking at 2008 or 2009 before they will have a working prototype. Read more: the link

[CMU VASC seminar] Free-Viewpoint Image Synthesis from Multi-View Images

Title : Free-Viewpoint Image Synthesis from Multi-View Images
Speaker : Keita Takahashi, University of Tokyo

Abstract :
This talk introduces an image-based rendering method using multi-viewimages. Using densely-arranged 2-D array of cameras as a model for data acquisition, we adopted a layered depth approach for synthesizing free-viewpoint images. Instead of explicit shape reconstruction, we proposed a signal processing approach based on our "focus measure" scheme;first, synthesize an image for each of the depth layers, then, find the "in-focus" parts on each of the images to integrate them into a final image. Since our method requires no off-line processing, it runs at interactive frame-rates on a commodity PC. Several experimental results are presented to show the effectiveness of our method.

Here is the related links for this research :
Author's homepage
Full paper of this research

MIT CSAIL report: Phonetic Classification Using Hierarchical, Feed-forward, Spectro-temporal Patch-based Architectures

Authors: Rifkin Ryan, Bouvrie Jake, Schutte Ken, Chikkerur Sharat, Kouh Minjoon, Ezzat Tony, Poggio Tomaso

Issue Date: 1-Feb-2007

Abstract: A preliminary set of experiments are described in which a biologically-inspired computer vision system (Serre, Wolf et al. 2005; Serre 2006; Serre, Oliva et al. 2006; Serre, Wolf et al. 2006) designed for visual object recognition was applied to the task of phonetic classification. During learning, the systemprocessed 2-D wideband magnitude spectrograms directly as images, producing a set of 2-D spectrotemporal patch dictionaries at different spectro-temporal positions, orientations, scales, and of varying complexity. During testing, features were computed by comparing the stored patches with patches fromnovel spectrograms. Classification was performed using a regularized least squares classifier (Rifkin, Yeo et al. 2003; Rifkin, Schutte et al. 2007) trained on the features computed by the system. On a 20-class TIMIT vowel classification task, the model features achieved a best result of 58.74% error, compared to 48.57% error using state-of-the-art MFCC-based features trained using the same classifier. This suggests that hierarchical, feed-forward, spectro-temporal patch-based architectures may be useful for phoneticanalysis.
pdf, link

CMU RI Seminar: UAV-Enabled Wilderness Search and Rescue (WiSAR)

Wilderness Search and Rescue (WiSeR) operations include finding and giving assistance to humans who are lost or injured in mountain, desert, lake, river, or other remote settings. WiSeR is a challenging task that requires many hours of effort by people with specialized training. These searches, which consume thousands of man-hours and hundreds of thousands of dollars per year in Utah alone, are often very slow because of the large distances and challenging terrain that must be searched. Moreover, timeliness of the search is critical; for every hour that passes, the search radius must increase by approximately 3km, and the probability of finding and successfully aiding the victim decreases.

This talk will present research on using small Unmanned Aerial Vehicles (UAVs) to assist in WiSeR tasks. Topics include an analysis of how WiSeR is currently done, how UAVs can be used to support the current efforts, and the development of key technologies that make UAV-enabled WiSeR possible. Discussion will include designing UAV autonomy, modeling victim behavior, creating interfaces that allow the UAV to be effectiently tasked, and presenting imagery in a way that increases the probability of finding a victim.

Bio:
Michael A. Goodrich is an associate professor in the Computer Science Department at Brigham Young University. Before joining BYU, he completed a Ph.D. degree in Electrical and Computer Engineering and spent two years as a post-doctoral research associate at Nissan Cambridge Basic Research. His research is driven by a desire to understand intelligence. Toward this goal, he works on problems in human-robot interaction, multi-agent learning, and intelligent vehicle systems.

MIT CSAIL defense : A few days of a robot

Speaker: Lijin Aryananda , MIT CSAIL
Relevant URL: http://people.csail.mit.edu/lijin

Abstract:

This thesis presents an implementation of a robotic head, Mertz, designed to explore incremental face recognition through natural interaction. We have seen many recent efforts in the path of integrating robots into the home for assisting with elder care, domestic chores, etc. In order to be effective in human-centric tasks, the robots must distinguish not only between people and inanimate objects, but also among different family members in the household. This thesis was driven by two specific limitations of the current technology. First, current automatic face recognition technology mostly explores the supervised solutions which are limited to a fixed training set and require cumbersome data collection and labelling procedures. Second, the lack of robustness and scalability to unstructured environments create a large gap between current research robots and commercial home products. The goal of this thesis is to advance toward a framework which would allow the robots to incrementally "get to know" each individual in an unsupervised way through daily interaction. In contrast to the target of a stand-alone and maximally optimized face recognition system, our approach is to develop an integrated robotic system as a step toward the ultimate end-to-end system capable of incremental individual recognition in a real human environment. Our main emphasis is to develop Mertz as a robotic creature with adequate overall robustness to be embedded in the dynamic and noisy human environment. Thus, we require the robot to operate for a few hours at a time and interact with a large number of passersby with minimal constraints at public locations. The robot then autonomously detects, tracks, and segments face images during these interactions and automatically generates a training set for its face recognition system. In this talk, we present the robot implementation and its unsupervised incremental face recognition framework. We describe an algorithm for clustering SIFT features extracted from a large set of face sequences automatically generated by the robot. We demonstrate the robot's capabilities and limitations in a series of experiments at a public lobby. In a final experiment, the robot interacted with a few hundred individuals in an eight day period and generated a training set of over a hundred thousand face images. We evaluate the clustering algorithm performance across a range of parameters on this automatically generated training data and also the Honda-UCSD video face database. Lastly, we present some recognition results using the self-labelled clusters.

News: ASIMO robot falls down stairs

Most of the time all you see are slick videos of the new bipedal robots flawlessly trotting about, but we like this one, when things go a little wrong... - Link.

The action starts 58 seconds in.

Originated from MAKE:

News: A robot for your digital camera?

Posted by Roland Piquepaille @ 9:43 am, February 2nd, 2007

According to the Pittsburgh Post-Gazette, researchers from Carnegie Mellon University and NASA Ames will release in March a $200 robot which will transform your digital cameras into powerful image-makers without your help. Attached to almost any model of digital cameras, the Gigapan robot platform will take continuous snapshots of a place or an event. Then the software provided by the research team will produce a panoramic image built from all these snapshots. And these images will be zoomable. This means you'll have the best of two worlds, big panoramas and startling details.

This GigaPan platform has been developed at Carnegie Mellon University by Illah Nourbakhsh, an associate professor of robotics with the help of the NASA Ames Intelligent Robot Group. This project is part of the Global Connection Project.

See the full article. Go to the GIGAPAN web site.

MIT talk: Do robots offer a quantum leap in studying whales?

Speaker: Roger Payne , Ocean Alliance, Lincoln, MA
Date: Tuesday, February 6 2007
Relevant URL: http://www.oceanalliance.org/wci

Abstract:

Payne will review his 39 year study of Patagonian right whales one conclusion of which is that a key to conserving any species seems to be to learn to live with it, and from that experience to build it into human consciousness. But learning to live with whales that migrate 8,000 miles each year requires one to overcome the obstacle of keeping up with them, something that has failed in spite of numerous attempts to achieve it. Storms are what usually cause those who tag and follow whales to lose them. However, robots show great promise in enabling boats to keep up with whales, something that may offer a chance for a major leap in understanding of whales, and that may even someday allow future generations to guide whales into waters in which there is no whaling industry.

Lab Meeting 1 Feb 2007 (Yu-Chun): Integrating the OCC Model of Emotions in Embodied Characters

Christoph Bartneck
Workshop on Virtual Conversational Characters: Applications, Methods, and Research Challenges, 2002
[Link]

Abstract:
The OCC (Ortony, Clore, & Collins, 1988) model has established itself as the standard model for emotion synthesis. A large number of studies employed the OCC model to generate emotions for their embodied characters. Many developers of such characters believe that the OCC model will be all they ever need to equip their character with emotions. This paper points out what the OCC model is able to do for an embodied emotional character and what it does not. Missing features include a history function, a personality designer and the interaction of the emotional categories.

MIT report: Online Active Learning in Practice

Title: Online Active Learning in Practice
Authors: Monteleoni, Claire and Kaariainen, Matti
Advisor: Tommi Jaakkola
Issue Date: 23-Jan-2007

Abstract: We compare the practical performance of several recently proposed algorithms for active learning in the online setting. We consider two algorithms (and their combined variants) that are strongly online, in that they do not store any previously labeled examples, and for which formal guarantees have recently been proven under various assumptions. We perform an empirical evaluation on optical character recognition (OCR) data, an application that we argue to be appropriately served by online active learning. We compare the performance between the algorithm variants and show significant reductions in label-complexity over random sampling.

URI: http://hdl.handle.net/1721.1/35784

CMU RI Seminar: Socially Guided Machine Learning

Andrea Thomaz
Post-Doctoral Associate
MIT

Abstract
There is a surge of interest in having robots leave the labs and factory floors to help solve critical issues facing our society, ranging from eldercare to education. A critical issue is that we will not be able to preprogram these robots with every skill they will need to play a useful role in society; robots will need the ability to interact and learn new things 'on the job' from everyday people. This talk introduces a paradigm, Socially Guided Machine Learning, that reframes the Machine Learning problem as a human-machine interaction, asking: How can systems be designed to take better advantage of learning from a human partner and the ways that everyday people approach the task of teaching?

In this talk I describe two novel social learning systems, on robotic and computer game platforms. Results from these systems show that designing agents to better fit human expectations of a social learning partner both improves the interaction for the human and significantly improves the way machines learn.

Sophie is a virtual robot that learns from human players in a video game via interactive Reinforcement Learning. A series of experiments with this platform uncovered and explored three principles of Social Machine Learning: guidance, transparency, and asymmetry. For example, everyday people were able to use an attention direction signal to significantly improve learning on many dimensions: a 50% decrease in actions needed to learn a task, and a 40% decrease in task failures during training.

On the Leonardo social robot, I describe my work enabling Leo to participate in social learning interactions with a human partner. Examples include learning new tasks in a tutelage paradigm, learning via guided exploration, and learning object appraisals through social referencing. An experiment with human subjects shows that Leo's social mechanisms significantly reduced teaching time by aiding in error detection and correction.

Lab Meeting 1 Feb 2007 (Any): Dynamic Maps for Long-Term Operation of Mobile Service Robots

Title: Dynamic Maps for Long-Term Operation of Mobile Service Robots
Authors: Peter Biber, Tom Duckett
Conference: Robotics: Science and System 2005 (RSS'05)
Local Copy: [PDF]

Abstract:

This paper introduces a dynamic map for mobile robots that adapts continuously over time. It resolves the stability-plasticity dilemma (the trade-off between adaptation to new patterns and preservation of old patterns) by representing the environment over multiple timescales simultaneously (5 in our experiments). A sample-based representation is proposed, where older memories fade at different rates depending on the timescale. Robust statistics are used to interpret the samples. It is shown that this approach can track both stationary and non-stationary elements of the environment, covering the full spectrum of variations from moving objects to structural changes. The method was evaluated in a five week experiment in a real dynamic environment. Experimental results show that the resulting map is stable, improves its quality over time and adapts to changes.

News: 'Sniffer-bot' algorithm helps robots seek scents

19:00 24 January 2007
NewScientist.com news service
Mason Inman

Moths are renowned for their ability to pick up a faint whiff of pheromones from faraway mates. Robots may soon match this feat with the help of a new mathematical method developed to help guide them toward a scent. In tests, the algorithm made virtual scent-hunter bots move just like moths do, snaking and spiralling toward their goal.

...

Massimo Vergassola at the Pasteur Institute in Paris, France, and colleagues created an algorithm that tells a robot how to move in order to gather as much olfactory information as possible. This allows it to home in on even the faintest of scents.

See the full article.
Journal reference: Nature (vol 445, p 406)

News: Web cam periscope

This little web cam accessory ($99) is like a periscope so you can look directly at the person you're video conferencing with... might be a fun re-make, links below to get you started - Link

Lab Meeting 25 Jan 2007(AShin):The Expectation Maximization Algorithm

Arthur:Frank Dellaert

College of Computing, Georgia Institute of Technology Technical Report

February 2002

Abstract:

This note represents my attempt at explaining the EM algorithm (Hartley, 1958;Dempster et al., 1977; McLachlan and Krishnan, 1997). This is just a slightvariation on Tom Minka’s tutorial (Minka, 1998), perhaps a little easier (or perhapsnot). It includes a graphical example to provide some intuition.

[Link]

Lab Meeting 25 Jan 2007 (ZhenYu):Psychophysiological control architecture for human-robot coordination-concepts and initial experiments

Nilanjan Sarkar
Dept. of Mech. Eng., Vanderbilt Univ., Nashville, TN;

Abstract:

The use of robots is expected to be pervasive in many spheres of society: in hospitals, homes, offices and battlefields, where the robots will need to interact and cooperate closely with a variety of people. The paper proposes an innovative approach to human-robot cooperation where the robot will be able to recognize the psychological state of the interacting human and modify its (i.e., robot's) own action to make the human feel comfortable in working with the robot. Wearable biofeedback sensors are used to measure a variety of physiological indices to infer the underlying psychological states (affective states) of the human. The eventual idea is to correlate the psychological states with the actions of the robot to determine which action(s) is responsible for a particular affective state. The robot controller will then modify that action if there is a need to alter the affective state. A concept of such a control architecture, a requirement analysis, and initial results from human experiments for stress detection are presented.

[link]

Lab Meeting 25 Jan 2007 (Casey): Contrast Context Histogram – A Discriminating Local Descriptor for Image Matching

From: The 18th International Conference on Pattern Recognition ( ICPR'06)
Title: Contrast Context Histogram – A Discriminating Local Descriptor for Image Matching
Author: Chun-Rong Huang, Chu-Song Chen and Pau-Choo Chung

Abstract:
This paper presents a new invariant local descriptor, contrast context histogram, for image matching. It represents the contrast distributions of a local region, and serves as a local distinctive descriptor of this region. Object recognition can be considered as matching salient corners with similar contrast context histograms on two or more images in our work. Our experimental results show that the developed descriptor is accurate and efficient for matching.

Paper Download: Link

CMU VASC talk: Subspectral Algorithms for Sparse Learning, Optimization & Inference

Baback Moghaddam
MERL
Monday, Jan 29, 3:30pm, NSH 1507

Subspectral Algorithms for Sparse Learning, Optimization & Inference

I will present a class of "subspectral" algorithms (i.e. sparse eigenvector techniques) for solving NP-hard combinatorial optimization problems in three general applied domains: (1) Supervised/unsupervised learning, in the traditional or orthodox sense (e.g. PCA & LDA), (2) Quadratic/Entropic Optimization (e.g. Least-Squares & MaxEnt) and (3) Inference, in the strict probabilistic/Bayesian sense (e.g. Automatic Relevance Determination and variational methods like Expectation Propagation). Subspectral algorithms for both exact (optimal) and greedy (approximate) solutions of these general sparse optimization problems are derived using analytic eigenvalue bounds. Specifically, an efficient "dual-pass" greedy algorithm is shown to yield near-optimal solutions for all possible cardinalities (at once) in a fraction of the time it takes for most continuous relaxation methods to find solutions of comparable quality for a single cardinality. I will present sample applications of subspectral optimization techniques in .sparse PCA. for feature selection (statistics), .sparse LDA. for classification (gene discovery), sparse kernel regression (robotics & control), sparse quadratic programming (portfolio optimization), graph model selection (sensor networks) as well as sparse Bayesian inference for computer vision (face recognition & OCR).

Bio:
Baback Moghaddam's research interests are in computational vision with a main focus on probabilistic visual learning. His related areas of interest and expertise include statistical modeling, Bayesian data analysis, machine learning and pattern recognition. He obtained his PhD in Electrical Engineering and Computer Science (EECS) from the Massachusetts Institute of Technology (MIT) in 1997 where he was a member of the Vision and Modeling Group at the MIT Media Laboratory where he developed a fully-automatic vision system which won DARPA's 1996 "FERET" Face Recognition Competition.

Dr. Moghaddam was the winner of the 2001 Pierre Devijver Prize from the International Association of Pattern Recognition for his "innovative approach to face recognition" and received the Pattern Recognition Society Award for "exceptional outstanding quality" for his journal paper "Bayesian Face Recognition." He currently serves on the editorial board of the journal Pattern Recognition and has contributed to numerous textbooks on image processing and computer vision including the core chapter in Springer-Verlag's latest biometric series, "Handbook of Face Recognition."

Dr. Moghaddam's past research included infrared (IR) image analysis for the Office of Naval Research (ONR), segmentation of synthetic aperture radar (SAR) imagery for MIT Lincoln Laboratory as well as designing a micro-gravity experiment for laser annealing of amorphous silicon which was flown aboard the US Space Shuttle in 1990.

http://www.merl.com/people/baback

CMU ML talk: Approximate inference using planar graph decomposition

Approximate inference using planar graph decomposition
by Amir Globerson and Tommi Jaakkola
NIPS 2006

A number of exact and approximate methods are available for inference calculations in graphical models. Many recent approximate methods for graphs with cycles are based on tractable algorithms for tree structured graphs. Here we base the approximation on a different tractable model, planar graphs with binary variables and pure interaction potentials (no external field). The partition function for such models can be calculated exactly using an algorithm introduced by Fisher and Kasteleyn in the 1960s. We show how such tractable planar models can be used in a decomposition to derive upper bounds on the partition function of non-planar models. The resulting algorithm also allows for the estimation of marginals. We compare our planar decomposition to the tree decomposition method of Wainwright et. al., showing that it results in a much tighter bound on the partition function, improved pairwise marginals, and comparable singleton marginals.

CMU ML talks: Greedy Layer-Wise Training of Deep Networks

Greedy Layer-Wise Training of Deep Networks
by Yoshua Bengio, Pascal Lamblin, Dan Popovici and Hugo Larochelle
NIPS 2006

Deep multi-layer neural networks have many levels of non-linearities allowing them to compactly represent highly non-linear and highly-varying functions. However, until recently it was not clear how to train such deep networks, since gradient- based optimization starting from random initialization appears to often get stuck in poor solutions. Hinton et al. recently introduced a greedy layer-wise unsupervised learning algorithm for Deep Belief Networks (DBN), a generative model with many layers of hidden causal variables. In the context of the above optimization problem, we study this algorithm empirically and explore variants to better understand its success and extend it to cases where the inputs are continuous or where the structure of the input distribution is not revealing enough about the variable to be predicted in a supervised task. Our experiments also confirm the hypothesis that the greedy layer-wise unsupervised training strategy mostly helps the optimization, by initializing weights in a region near a good local minimum, giving rise to internal distributed representations that are high-level abstractions of the input bringing better generalization.

News: Robot nurses ready for wards 'in three years'

http://www.thisislondon.co.uk/

Robot nurses could be bustling around hospital wards in as little as three years.

The mechanised "angels" being developed by EU-funded scientists, will perform basic tasks such as mopping up spillages, taking messages, and guiding visitors to hospital beds.

They could also distribute medicines and even monitor the temperature of patients remotely with laser thermometers.

...

He told The Engineer magazine: "The idea is not only to have mobile robots but also a full system of integrated information terminals and guide lights, so the hospital is full of interaction and intelligence.

"Operating as a completely decentralised network means that the robots can co-ordinate things between themselves, such as deciding which one would be best equipped to deal with a spillage or to transport medicine."

He said the robots could provide a valuable service guiding people around the hospital. A visitor would state the name of a patient at an information terminal and then follow a robot to the correct bedside.

...

See the full article.

MIT CSAIL PhD thesis: Robot Manipulation in Human Environments

Authors: Edsinger, Aaron
Advisor: Rodney Brooks
Issue Date: 16-Jan-2007

Abstract: Human environments present special challenges for robot manipulation. They are often dynamic, difficult to predict, and beyond the control of a robot engineer. Fortunately, many characteristics of these settings can be used to a robot's advantage. Human environments are typically populated by people, and a robot can rely on the guidance and assistance of a human collaborator. Everyday objects exhibit common, task-relevant features that reduce the cognitive load required for the object's use. Many tasks can be achieved through the detection and control of these sparse perceptual features. And finally, a robot is more than a passive observer of the world. It can use its body to reduce its perceptual uncertainty about the world. In this thesis we present advances in robot manipulation that address the unique challenges of human environments. We describe the design of a humanoid robot named Domo, develop methods that allow Domo to assist a person in everyday tasks, and discuss general strategies for building robots that work alongside people in their homes and workplaces. PDF

Paper: Experimental Characterization of Commercial Flash Ladar Devices

D. Anderson, H. Herman, and A. Kelly
International Conference of Sensing and Technology, November, 2005.

Abstract: Flash ladar is a new class of range imaging sensors. Unlike traditional ladar devices that scan a collimated laser beam over the scene, flash ladar illuminates the entire scene with diffuse laser light. Recently, several companies have begun offering demonstration flash ladar units commercially. In this work, we seek to characterize the performance of two such devices, examining the effects of target range, reflectance and angle of incidence, as well as mixed pixel effects.

PDF

CMU RI seminar: The neuroarchitecture of complex cognition

Marcel Adam Just
D. O. Hebb Professor of Psychology
Carnegie Mellon University

Recent findings in brain imaging, particularly fMRI, are beginning to reveal some of the fundamental properties of the organization of the cortical systems that underpin complex cognition. A set of operating principles govern the system organization, characterizing the system as a set of collaborating cortical centers that operate as a large-scale cortical network. Two of the network's critical features are that it is resource-constrained and dynamically-configured, with resource constraints and demands dynamically shaping the network topology. The operating principles are embodied in a cognitive neuroarchitecture, 4CAPS, consisting of a number of interacting computational centers that correspond to activating cortical areas. Each 4CAPS center is a hybrid production system, possessing both symbolic and connectionist attributes. 4CAPS models of several cognitive tasks (sentence comprehension, spatial problem solving, and complex multitasking) have been developed and compared to brain activation and behavioral results.

Congratulations to Casey Wang!

Congratulations! Casey Wang successfully defended his master thesis on "Hand Gesture Recognition using Adaboost with SIFT". Good job!

-Bob

IEEE ITSS Newsletter vol 8 nr 4, December 2006

In this issue you will find a lot of interesting material:
- ITSS related news; in particular many new initiatives from the ITS Society
- technical papers
- conference reports and announcements
- research overview focusing nomadic devices in the new intelligent vehicles

You can find the ITSS Newsletter at the IEEE ITSS official web site address:
http://www.ewh.ieee.org/tc/its/
or directly at:
http://www.its.washington.edu/itsc/v8n4.pdf

MIT CSAIL report: Latent-Dynamic Discriminative Models for Continuous Gesture Recognition

Authors: Morency, Louis-Philippe; Quattoni, Ariadna; Darrell, Trevor
Issue Date: 7-Jan-2007

Abstract: Many problems in vision involve the prediction of a class label for each frame in an unsegmented sequence. In this paper we develop a discriminative framework for simultaneous sequence segmentation and labeling which can capture both intrinsic and extrinsic class dynamics. Our approach incorporates hidden state variables which model the sub-structure of a class sequence and learn the dynamics between class labels. Each class label has a disjoint set of associated hidden states, which enables efficient training and inference in our model. We evaluated our method on the task of recognizing human gestures from unsegmented video streams and performed experiments on three different datasets of head and eye gestures. Our results demonstrate that our model for visual gesture recognition outperform models based on Support Vector Machines, Hidden Markov Models, and Conditional Random Fields.

PDF, PS

Lab meeting 12 Jan, 2007 (Stanley): A robocentric motion planner for dynamic environments using the velocity space

Author: E. Owen, L. Montano

From: IEEE/RSJ International Conference on Intelligent Robots and Systems. Oct. 9-15, Beijing, China.

Abstract:
This paper addresses a method to optimize the robot motion planning in dynamic environments, avoiding the moving and static obstacles while the robot drives towards the goal. The method maps the dynamic environment into a model in the velocity space, computing the times to potential collision and potential escape and the associated robot velocities. The problem of finding a trajectory to the goal is stated as a constrained nonlinear optimization problem. The initial seed trajectory for the optimization is directly generated in the velocity space using the model built. The method is applied to robots which are subject to both kinematic constraints (i.e. involving the configuration parameters of the robot and their derivatives), and dynamic constraints, (i.e. the constraints imposed by the acceleration/deceleration capabilities). Some experimental results are discussed.

Link

News: iRobot Introduces the iRobot Create!

A robust, programmable Robot platform that invites you to stretch your imagination

iRobot, a pioneering robot company that has sold millions of iRobot Roomba vacuuming robots, has introduced a remarkable robot platform that fills a major gap.

The iRobot Create is a dependable, rugged and versatile robot base that can be used for uncounted robotics hobby and research applications. It includes a selection of software routines developed for iRobot’s commercial appliance bots and a well-engineered, robust chassis designed for longevity. Many will consider this to be a DIY-roboticist’s dream come true.

Here, we offer a summary of the iRobot Create’s features in this First Look, initial comments by contributing editor Dan Lynch (who has been playing with one for a few days as of this post) and an interesting overview of applications designed for this new robot platform by iRobot employees worldwide. Stay tuned for an in-depth feature article in the Summer 2007 issue of Robot.

The iRobot Create comes fully assembled. It has 32 built-in sensors, two powered wheels, a castor (and optional 4th castor wheel), 10 pre-programmed behaviors, an expandable input/ouput port for custom sensors and actuators, a cargo bay with mounting points and a tailgate for ballast. This new bot platform works with optional accessories such as the iRobot Command Module, iRobot Roomba Virtual Wall units, the self-charging home base, and iRobot Roomba standard remote. You can use the Roomba rechargeable battery options or standard alkaline batteries. You’ll need a computer with a serial port (USB connectivity is expected soon) and Microsoft Windows XP, Linux or Mac OS X. - Link

Related: iRobot "Create" - Educational robot - Link

News: The First Kyosho Athlete Humanoid Cup!

The launch of the MANOI AT01 humanoid robot kit, featured in our Winter 2006 issue, was an instant success with many stores in Japan selling out of stock within a few days of its September introduction. So it was no surprise when a large number of customers turned out with their robots fully assembled and customized to compete in the first Kyosho Athletics Humanoid Cup event on December 10th in the trendy Omotesando Hills complex in Tokyo.

The inaugural Kyosho Athlete Humanoid Cup event was of keen interest to robot fans everywhere. The images below tell the story of how this exciting competition unfolded.

Left to right: Dr. GIY's MANOI AT01, Sugiura's AT01 and a silver MANOI PF01 were exhibited at a MANOI launch press conference in September 2006.

The competitions, watched by standing-room only crowds, included 5-meter sprints against the clock with both R/C and autonomous divisions, plus two minute demonstrations/performances scored by an expert panel of judges.

See the full article.

paper: Predictive Mover Detection and Tracking in Cluttered Environments

L. Navarro, C. Mertz, and M. Hebert
Proc. of the 25th. Army Science Conference, November, 2006.
PDF

Abstract:This paper describes the design and experimental evaluation of a system that enables a vehicle to detect and track moving objects in real-time. The approach investigated in this work detects objects in LADAR scan lines and tracks these objects (people or vehicles) over time. The system can fuse data from multiple scanners for 360° coverage. The resulting tracks are then used to predict the most likely future trajectories of the detected objects. The predictions are intended to be used by a planner for dynamic object avoidance. The perceptual capabilities of our system form the basis for safe and robust navigation in robotic vehicles, necessary to safeguard soldiers and civilians operating in the vicinity of the robot.

paper: Bootstrap learning of foundational representations

B.J. Kuipers, P. Beeson, J. Modayil, J. Provost
Connection Science, 2006 - Taylor & Francis
PDF

Abstract: To be autonomous, intelligent robots must learn the foundations of commonsense knowledge from their own sensorimotor experience in the world. We describe four recent research results that contribute to a theory of how a robot learning agent can bootstrap from the “blooming buzzing confusion” of the pixel level to a higher-level ontology including distinctive states, places, objects, and actions. This is not a single learning problem, but a lattice of related learning tasks, each providing prerequisites for tasks to come later. Starting with completely uninterpreted sense and motor vectors, as well as an unknown environment, we show how a learning agent can separate the sense vector into modalities, learn the structure of individual modalities, learn natural primitives for the motor system, identify reliable relations between primitive actions and created sensory features, and can define useful control laws for homing and path-following. Building on this framework, we show how an agent can use to self-organizing maps to identify useful sensory featurs in the environment, and can learn effective hill-climbing control laws to define distinctive states in terms of thos features, and trajectoryfollowing control laws to move from one distinctive state to another. Moving on to place recognition, we show how an agent can combine unsupervised learning, map-learning, and supervised learning to achieve high-performance recognition of places from rich sensory input. And finally, we take the first steps toward learning an ontology of objects, showing tha a bootstrap learning robot can learn to individuate objects through motion, separating them from the static environment and from each other, and learning properties that will be useful for classification. These are four key steps in a much larger research enterprise that lays the foundation for human and robot commonsense knowledge.

News: Wow Wee Unveils Robopanda

Cute, but not cuddly robot will be the company's most charming and interactive part of its 2007 line.

One of the highlights of AI, Steve Spielberg's melancholy look at robots in the distant future, was a little autonomous robot bear, "Teddy," that served as friend and companion to the film's main character David the "mech" boy robot. The bear could walk, talk interact and show real intelligence and affection. Now Wow Wee is apparently ripping a page from that movie's script to introduce the new Robopanda.

Part of Wow Wee's 2007 line of robot toys, the $229 Robopanda is swathed in plastic instead of cuddly fur, and is roughly 19.5 inches tall (standing), 11 inches wide and 6 inches deep. Wow Wee officials, however, promise a level of robot/human interaction scarcely seen in previous robot toys.

Designed for children (and, perhaps, adults) ages 4 and up, the 8-pound Robopanda will be covered with eight touch sensors and, using infrared and stereo sensors, should be able to avoid obstacles, track objects and locate sound sources. It will also tell stories and recognize and interact with its own companion: a plush-panda toy that will ship with the robot.

The robo-mammal will also feature, Wow Wee execs said, "incredible movement," using its nine "ultra-quiet" motors (and one tilt sensor) to sit, crawl, walk (on all fours), roll over and hug. Unlike previous Wow Wee robots, Robopanda is not expected to ship with a remote. Its artificial intelligence may also be greater than previous Wow Wee products: "Robpanda responds with mood-specific behaviors…based on [user] interaction," Wow Wee notes in a recent press release.

See the full article.

News: Spyke Wi-Fi Spy Robot Debuts at CES 2007

Consumer robots could also be a big topic at the CES 2007. Here is a newcomer. French MECCANO introduces the Spyke robot in the United States under the ERECTOR brand.

The Spyke robot is controlled via a PC over Wi-Fi. Spyke has a webcam and moves on a rubber band. Professional robots can climb stairs with such a drive mechanism.

See the full article.

News: Gates says day of the home-help robot is near

James Randerson, science correspondent
Friday January 5, 2007
The Guardian

An office worker checks her home-gadget webpage from her work computer. The tasks she set for her home robots in the morning have all been completed: washing and ironing, vacuuming the lounge and mowing the lawn.

She orders dinner from the kitchen chefbot - sushi today, using a recipe from a Japanese website - then checks her elderly mother's house. The companionbot has given mum her medicine and helped her out of bed and into a chair.

This is the vision of the future offered by Bill Gates who, in the latest issue of Scientific American, argues that the robotics industry is on the cusp of a big expansion. He likens the current state of robotic technology to the situation in the fledgling computer industry when he and his fellow entrepreneur Paul Allen launched Microsoft in the mid-1970s.

See the full article.

Lab meeting 5 Jan, 2007 (Nelson): Motion–Egomotion Discrimination and Motion Segmentation from Image-Pair Streams

David Demirdjian and Radu Horaud
[LINK]

Computer Vision and Image Understanding
Volume 78 , Issue 1 (April 2000)
Special issue on robust statistical techniques in image understanding

Pages: 53 - 68

Abstract:

Given a sequence of image pairs we describe a method that segments the observed scene into static and moving objects while it rejects badly matched points.We show that, using a moving stereo rig, the detection of motion can be solved in a projective framework and therefore requires no camera calibration. Moreover the method allows for articulated objects. First we establish the projective framework enabling us to characterize rigid motion in projective space. This characterization is used in conjunction with a robust estimation technique to determine egomotion. Second we describe a method based on data classification which further considers the non-static scene points and groups them into several moving objects. Third we introduce a stereo-tracking algorithm that provides the point-to-point correspondences needed by the algorithms. Finally we show some experiments involving a moving stereo head observing both static and moving objects.

Lab meeting 5 Jan, 2007(Atwood): Plan-view trajectory estimation with dense stereo background models

Title: Plan-view trajectory estimation with dense stereo background models
Auther: Darrell, T.; Demirdjian, D.; Checka, N.; Felzenszwalb, P.

from ICCV 2001.

Abstract:

In a known environment, objects may be tracked in multiple views using a set of background models. Stereo-based models can be illumination-invariant, but often have undefined values which inevitably lead to foreground classification errors. We derive dense stereo models for object tracking using long-term, extended dynamic-range imagery, and by detecting and interpolating uniform but unoccluded planar regions. Foreground points are detected quickly in new images using pruned disparity search. We adopt a “late-segmentation” strategy, using an integrated plan-view density representation. Foreground points are segmented into object regions only when a trajectory is finally estimated, using a dynamic programming-based method. Object entry and exit are optimally determined and are not restricted to special spatial zones


Link

News: Robot, My Slippers Please

Home sensors, long-distance health monitors and other gadgets help seniors remain independent.

May 2006

RI-MAN isn’t your average caregiver. The pale-green, 220-pound robot is a mass of wiring, metal and computer chips. It was created in Japan as an eventual high-tech alternative to costly home-health services and nursing-home care.

Although you can’t order your own RI-MAN or other home-care robot yet, you can buy many other assistive-technology devices that enable older adults with various ailments to continue to live in their own homes. Such devices include home sensors that monitor a person’s day-to-day activities and special goggles that help the visually impaired to see. These products are part of tech companies’ response to the new demographics: a rising number of seniors, families scattered around the globe and grown children with full-time careers who care for elderly parents. Here are some examples of what’s available now.

See the full article.

Lab meeting 28 Dec, 2006 (Jim): Unified Inverse Depth Parametrization for Monocular SLAM

Unified Inverse Depth Parametrization for Monocular SLAM
Montiel etal., RSS 2006

PDF
A.J.Davison's website

Abstract:
      Recent work has shown that the probabilistic SLAM approach of explicit uncertainty propagation can succeed in permitting repeatable 3D real-time localization and mapping even in the ‘pure vision’ domain of a single agile camera with no extra sensing. An issue which has caused difficulty in monocular SLAM however is the initialization of features, since information from multiple images acquired during motion must be combined to achieve accurate depth estimates. This has led algorithms to deviate from the desirable Gaussian uncertainty representation of the EKF and related probabilistic filters during special initialization steps.
      In this paper we present a new unified parametrization for point features within monocular SLAM which permits efficient and accurate representation of uncertainty during undelayed initialisation and beyond, all within the standard EKF (Extended Kalman Filter). The key concept is direct parametrization of inverse depth, where there is a high degree of linearity. Importantly, our parametrization can cope with features which are so far from the camera that they present little parallax during motion, maintaining sufficient representative uncertainty that these points retain the opportunity to ‘come in’ from infinity if the camera makes larger movements. We demonstrate the parametrization using real image sequences of large-scale indoor and outdoor scenes.

Lab meeting 28 Dec, 2006 (Any): Sonar Sensor Interpretation

Title: Sonar Interpretation Learned from Laser Data
Authors: S. Enderle, G. Kraetzschmar, S. Sablatnog and G. Palm
From: 1999 Third European Workshop on Advanced Mobile Robots, 1999. (Eurobot '99)
Links: [Paper 1][Paper 2][Paper 3]
Abstract:
Sensor interpretation in mobile robots often involves an inverse sensor model, which generates hypotheses on specific aspects of the robot's environment based on current sensor data. Building inverse sensor models for sonar sensor assemblies is a particularly difficult problem that has received much attention in past years. A common solution is to train neural networks using supervised learning. However; large amounts of training data are typically needed, consisting e.g. of scans of recorded sonar data which are labeled with manually constructed teacher maps. Obtaining these training data is an error-prone and time-consuming process. We suggest that it can be avoided, if an additional sensor like a laser scanner is also available which can act as the feeding signal. We have successfully trained inverse sensor models for sonar interpretation using laser scan data. In this paper; we describe the procedure we used and the results we obtained.

Lab meeting 28 Dec, 2006 (Leo): Square Root SAM

Square Root SAM: Simultaneous Localization and Mapping via Square Root Information Smoothing

Frank Dellaert

Robotics: Science and Systems, 2005

Abstract— Solving the SLAM problem is one way to enable
a robot to explore, map, and navigate in a previously unknown
environment. We investigate smoothing approaches as a viable
alternative to extended Kalman filter-based solutions to the
problem. In particular, we look at approaches that factorize either
the associated information matrix or the measurement matrix
into square root form. Such techniques have several significant
advantages over the EKF: they are faster yet exact, they can be
used in either batch or incremental mode, are better equipped
to deal with non-linear process and measurement models, and
yield the entire robot trajectory, at lower cost. In addition,
in an indirect but dramatic way, column ordering heuristics
automatically exploit the locality inherent in the geographic
nature of the SLAM problem.
In this paper we present the theory underlying these methods,
an interpretation of factorization in terms of the graphical model
associated with the SLAM problem, and simulation results that
underscore the potential of these methods for use in practice.


[Link]

Lab meeting 15 Dec, 2006 (Casey): Estimating 3D Hand Pose from a Cluttered Image

Title: Estimating 3D Hand Pose from a Cluttered Image
Authors: Vassilis Athitsos and Stan Scalaroff
(CVPR 2003)

Abstract:
A method is proposed that can generate a ranked list of
plausible three-dimensional hand configurations that best
match an input image. Hand pose estimation is formulated
as an image database indexing problem, where the closest
matches for an input hand image are retrieved from a large
database of synthetic hand images. In contrast to previous
approaches, the system can function in the presence of
clutter, thanks to two novel clutter-tolerant indexing methods.
First, a computationally efficient approximation of
the image-to-model chamfer distance is obtained by embedding
binary edge images into a high-dimensional Euclidean
space. Second, a general-purpose, probabilistic line matching
method identifies those line segment correspondences
between model and input images that are the least likely to
have occurred by chance. The performance of this cluttertolerant
approach is demonstrated in quantitative experiments
with hundreds of real hand images.

Paper download: [Link]

Lab meeting 15 Dec, 2006 (YuChun): Modeling Affect in Socially Interactive Robots

Author:
Rachel Gockley, Reid Simmons, and Jodi Forlizzi

Proc. of the 15th IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN06), September, 2006.

Abstract:
Humans use expressions of emotion in a very social manner, to convey messages such as “I'm happy to see you” or “I want to be comforted,” and people's long-term relationships depend heavily on shared emotional experiences. We believe that for robots to interact naturally with humans in social situations they should also be able to express emotions in both short-term and long-term relationships. To this end, we have developed an affective model for social robots. This generative model attempts to create natural, human-like affect and includes distinctions between immediate emotional responses, the overall mood of the robot, and long-term attitudes toward each visitor to the robot. This paper presents the general affect model as well as particular details of our implementation of the model on one robot, the Roboceptionist.


[Link]

[Thesis Oral] A Market-Based Framework for Tightly-Coupled Planned Coordination in Multirobot Teams

Author:
Nidhi Kalra
Robotics Institute
Carnegie Mellon University

Abstract:
This thesis explores the coordination challenges posed by real-world multirobot domains that require planned tight coordination between teammates throughout execution. These domains involve solving a multi-agent planning problem in which the actions of robots are tightly coupled. Because of uncertainty in the environment and the team, they also require persistent tight coordination between teammates throughout execution.

This thesis proposes an approach to these problems in which the complexity and strength of the coordination adapt to the difficulty of the problem. Our approach, called Hoplites, is a market-based framework that selectively injects pockets of complex coordination into a primarily distributed system by enabling robots to purchasing each other's participation in tightly-coupled plans over the market. We discuss how it is widely applicable to real-world problems because it is general, computationally feasible, scalable, operates under uncertainty, and improves solutions with new information. Experiments show that our approach significantly outperforms existing coordination methods.

Lab meeting 8 Dec, 2006 (Chihao): Particle filtering algorithms for tracking an acoustic source in a reverberant environment

Author:
Ward, D.B. Lehmann, E.A. Williamson, R.C.
Dept. of Electr. & Electron. Eng., Imperial Coll. London, UK

From: Speech and Audio Processing, IEEE Transactions

Abstract:

Traditional acoustic source localization algorithms attempt to find the current location of the acoustic source using data collected at an array of sensors at the current time only. In the presence of strong multipath, these traditional algorithms often erroneously locate a multipath reflection rather than the true source location. A recently proposed approach that appears promising in overcoming this drawback of traditional algorithms, is a state-space approach using particle filtering. In this paper we formulate a general framework for tracking an acoustic source using particle filters. We discuss four specific algorithms that fit within this framework, and demonstrate their performance using both simulated reverberant data and data recorded in a moderately reverberant office room (with a measured reverberation time of 0.39 s). The results indicate that the proposed family of algorithms are able to accurately track a moving source in a moderately reverberant room.

[Link]

Lab meeting 8 Dec, 2006 (AShin): Learning and Inferring Transportation Routines

Author:L. Liao, D. Fox, and H. Kautz

Proc. of the National Conference on Artificial Intelligence (AAAI-04)
Outstanding Paper Award

Abstract
This paper introduces a hierarchical Markov model that can learn and infer a user's daily movements through the community. The model uses multiple levels of abstraction in order to bridge the gap between raw GPS sensor measurements and high level information such as a user's mode of transportation or her goal. We apply Rao-Blackwellised particle filters for efficient inference both at the low level and at the higher levels of the hierarchy. Significant locations such as goals or locations where the user frequently changes mode of transportation are learned from GPS data logs without requiring any manual labeling. We show how to detect abnormal behaviors (\eg\ taking a wrong bus) by concurrently tracking his activities with a trained and a prior model. Experiments show that our model is able to accurately predict the goals of a person and to recognize situations in which the user performs unknown activities.

[Link]

No Polit, No Problem?

[origional link]

The promise is fantastic: new generations of remote-controlled aircraft could soon be flying in civilian airspace, performing all sorts of useful tasks.The reality is that a lack of radio frequencies to control the planes and serious concerns over their safety are going to keep them grounded for years to come.
Surprisingly, given the commercial hopes it has for civil unmanned aerial vehicles (UAVs), the aviation industry has failed to obtain the radio frequencies it needs to control them - and it will be 2011 before it can even begin to lobby for space on the radio spectrum. What's more, none of the world's aviation authorities will allow civil UAVs to fly in their airspace without a reliable system for avoiding other aircraft - and the industry has not yet even begun developing such a system. Experts say this could take up to seven years.
Dedicated frequencies are handed out at the International Telecommunications Union's World Radiocommunications Conference.But no one in the UAV industry had applied for any new frequencies.If UAVs are to mingle safely with other civilian aircraft, the industry needs to develop a safe, standardised collision avoidance system. This is complicated because aviation regulators demand that if UAVs are to have access to civil airspace, they must be "equivalent" in every way to regular planes.The problem for now is that aviation regulators have yet to define precisely what they mean by "equivalent", so UAV makers are not yet willing to commit themselves to developing collision-avoidance technology."A crewless aircraft on a collision course must behave as if it had a pilot on board"
On the brighter side, last week the UN's International Civil Aviation Organization said its navigation experts would meet in early 2007 to consider regulations for UAVs in civil airspace.
however, it will be meaningless unless the industry can obtain the necessary frequencies to control the planes and feed images and other sensor data back to base, says Bowker. "The lack of robust, secure radio spectrum is a show-stopper."