[Lab Meeting] May 19th, 2008 (Atwood): Progress Report

I will talk about my idea on semi-supervised Parts Condition Random Field

Outline

1. Introduction
2. Relates works
2.1 Hidden Conditional Random Field
2.2 Link analysis of feature matching
2.3 Spectral clustering
3. Semi-supervised Parts Conditional Random Field
3.1 Clustering for part-structure
3.2 Parts correspondence
4. Experiments

[Lab Meeting] May 19th, 2008 (fish60): Learning Grasp Strategies with Partial Shape Information

Abstract:
We consider the problem of grasping novel objects incluttered environments.
...
In this paper,we propose an approach to grasping that estimates thestability of different grasps, given only noisy estimatesof the shape of visible portions of an object, such as thatobtained from a depth sensor. By combining this witha kinematic description of a robot arm and hand, ouralgorithm is able to compute a specific positioning ofthe robot’s fingers so as to grasp an object.

Link

News: Microsoft Research Explores - Robots Among Us - Human-robot Interaction

The link.

Moving the technology toward these so-called “social robots” are researchers in a variety of disciplines engaged in the growing field of human-robot interaction (HRI). To explore some of the challenges in realizing the potential of HRI, Microsoft Research launched the “Robots Among Us” request for proposals (RFP) last October with the bold declaration, “The robots are coming!”

Eight winners will receive a share of more than US$500,000 awarded under the program. Winning research proposals were selected from 74 submissions from academic researchers from 24 countries. The research projects explore broad range of devices, technologies and functions as robots begin to work with and alongside human beings.

1. “Snackbot: A Service Robot,” Jodi Forlizzi and Sara Kiesler, Carnegie Mellon University. Snackbot will roam the halls of two large office buildings at Carnegie Mellon University, selling (or in some cases, giving away) snacks and performing other services. Microsoft’s grant will help the team link its current robot prototype to the Web, e-mail, instant messaging and mobile services. The group will also deploy the robot in a field study to understand the uptake of robotic products and services.
2. “Human-Robot-Human Interface for an autonomous vehicle in challenging environments,” Ioannis Rekleitis and Gregory Dudek, McGill University, Canada.Utilizing Microsoft Robotics Studio, this group will work to provide an interface for controlling a robot operating on land and underwater, as well as a visualization tool for interpreting the visual feedback. The work will also create a new method for communicating with AQUA when a direct link to a controlling console is not available.
3. “Personal Digital Interfaces for Intelligent Wheelchairs,” Nicholas Roy,Massachusetts Institute of Technology.Using a Windows Mobile PDA outfitted with a remote microphone and speech processor, this group will create a single, flexible point of interaction to control wheelchairs. The project will address human-robot interaction challenges in how the spatial context of the interaction varies depending on the location of the wheelchair, the location of the hand-held device and the location of the resident. This project is part of an ongoing collaboration with a specialized care residence in Boston.
4. Human-Robot Interaction to Monitor Climate Change via Networked Robotic Observatories, Dezhen Song, Texas A&M University, and Ken Goldberg, University of California, Berkeley. This team will develop a new Human-TeleRobot system to engage the public in documenting climate change effects on natural environments and wildlife, and provide a testbed for study of Human Robot Interaction. To facilitate this, a new type of human-robot system will be built to allow anyone via a browser to participate in viewing and collecting data via the Internet. The Human Robot Interface will combine telerobotic cameras and sensors with a competitive game where “players” score points by taking photos and classifying the photos of others.
5. FaceBots: Robots utilizing and publishing social information in FaceBook, Nikolaos Mavridis and Tamer Rabie, United Arab Emirates University. The system to be developed by Mavridis and Rabie is expected to achieve two significant novelties: arguably being the first robot that is truly embedded in a social web, and being the first robot that can purposefully exploit and create social information available online. Furthermore, it is expected to provide empirical support for their main hypothesis - that the formation of shared episodic memories within a social web can lead to more meaningful long-term human-robot relationships.
6. Multi-Touch Human-Robot Interaction for Disaster Response, Holly Yanco, University of Massachusetts. This group wants create a common computing platform that can interact with many different information systems, personnel from different backgrounds and expertise, and robots deployed for a variety of task in the event of a disaster. The proposed research intends to bridge the technological gaps through the use of collaborative tabletop multi-touch displays such as the Microsoft Surface. The group will develop an interface between the multi-touch display and Microsoft Robotics Studio to create a multi-robot interface for command staff to monitor and interact with all of the robots deployed at a disaster response.
7. Survivor Buddy: A Web-Enabled Robot as a Social Medium for Trapped Victims, Robin Murphy, University of South Florida.The main focus of this group is the assistance of humans who will be dependent on a robot for long periods of time. One function is to provide two-way audio communication between the survivor and the emergency response personnel. Other ideas are being studied, such as playing therapeutic music with a beat designed to regulate heartbeats or breathing. The idea is that a web-enabled, multi-media robot allows: 1) the survivor to take some control over the situation and find a soothing activity while waiting for extrication; and 2) responders to support and influence the state of mind of the victim.
8. Prosody Recognition for Human-Robot Interaction, Brian Scassellati, Yale University. This group will work to build a novel prosody recognition algorithm for release as a component for Microsoft Robotics Studio. Vocal prosody is the information contained in your tone of voice that conveys affect, and is a critical aspect to human-human interactions. In order to move beyond direct control of robots toward autonomous social interaction between humans and robots, the robots must be able to construct models of human affect by indirect, social means.

ICRA 2008 Conference and RAS Awards Finalists:

Below is the ICRA08 award finalist list. Some of you are assigned to read the papers and will lead discussions in the lab meetings. Of course all of you are encouraged to study these papers.

Best,

-Bob

Best Conference Paper Finalists:

• Employing Wave Variables for Coordinated Control of Robots with Distributed Control Architecture by Christian Ott and Yoshihiko Nakamura (Hero)
• Trajectory Generation for Dynamic Bipedal Walking through Qualitative Model Based Manifold Learning by Subramanian Ramamoorthy and Benjamin Kuipers (Stanley, Jim Yu)
  
• Consensus Learning for Distributed Coverage Control by Mac Schwager, Jean-Jacques E. Slotine and Daniela Rus (Hero Chen)
• Planning in Information Space for a Quadrotor Helicopter in a GPS-Denied Environment by Ruijie He, Sam Prentice and Nicholas Roy (Stanley, Jim Yu)

Best Student Paper Finalists (The name(s) of nominated student(s) are in bold font):

• Hybrid Simulation of a Dual-Arm Space Robot Colliding with a Floating Object by Ryohei Takahashi, Hiroto Ise, Daisuke Sato, Atsushi Konno and Masaru Uchiyama
• Partial barrier coverage: Using game theory to optimize probability of undetected intrusion in polygonal environments by Stephen Kloder and Seth Hutchinson (Der-Yeuan)
• Decentralized Feedback Controllers for Multi-Agent Teams in Environments with Obstacles by Nora Ayanian and Vijay Kumar (Hero, Jim Yu)
• Gecko-Inspired Climbing Behaviors on Vertical and Overhanging Surfaces by Daniel Santos, Barrett Heyneman, Sangbae Kim, Noe Esparza and Mark Cutkosky
• High Quality 3D LIDAR from Low Cost 2D Ranging Under General Vehicle Motion by Alastair Harrison and Paul Newman (Andi)

Best Automation Paper Finalists

• Dynamic Analysis of a High-Bandwidth, Large-Strain, PZT Cellular Muscle Actuator with Layered Strain Amplification by Thomas Secord, Jun Ueda and Harry Asada
• Event-Based Two Degree-Of-Freedom Control for Micro-/Nanoscale Systems Based on Differential Flatness by Ruoting Yang, T. J. Tarn and Mingjun Zhang
• On the Design of Traps for Feeding 3D Parts on Vibratory Tracks by Onno Goemans and Frank van der Stappen
• Fabrication of Functional Gel-Microbead for Local Environment Measurement in Microchip by Hisataka Maruyama, Fumihito Arai and Toshio Fukuda

Best Manipulation Paper (sponsored by Ben Wegbreit) Finalists:

• Skilled-Motion Plannings of Multi-Body Systems Based upon Riemannian Distance by Masahiro Sekimoto, Suguru Arimoto, Ji-Hun Bae and Sadao Kawamura
• Transportation of Hard Disk Media using Electrostatic Levitation and Tilt Control by Ewoud Frank van West, Akio Yamamoto and Toshiro Higuchi
• Adaptive Grasping by Multi Fingered Hand with Tactile Sensor Based on Robust Force and Position Control by Taro Takahashi, Toshimitsu Tsuboi, Takeo Kishida, Yasunori Kawanami, Satoru Shimizu, Masatsugu Iribe, Tetsuharu Fukushima and Masahiro Fujita
• Manipulating Articulated Objects With Interactive Perception by Dov Katz and Oliver Brock (Jim Yu)
• Synergistic Design of a Humanoid Hand with Hybrid DC Motor - SMA Array Actuators Embedded in the Palm by Josiah Rosmarin and Harry Asada
  

Best Vision Paper (sponsored by Ben Wegbreit) Finalists

• Accurate Calibration of Intrinsic Camera Parameters by Observing Parallel Light Pairs by Ryusuke Sagawa and Yasushi Yagi (54ways)
• Information-Optimal Selective Data Return for Autonomous Rover Traverse Science and Survey by David R. Thompson, Trey Smith and David Wettergreen (Yu-Hsiang)
• Image Moments-based Ultrasound Visual Servoing by Rafik Mebarki, Alexandre Krupa and Francois Chaumette (Jeff)
• Robust and Efficient Stereo Feature Tracking for Visual Odometry by Andrew E. Johnson, Steven B. Goldberg, Yang Cheng and Larry H. Matthies (Jeff, Yu-Hsiang)
• Accelerated Appearance-Only SLAM by Mark Cummins and Paul Newman (Jeff, Yu-Hsiang)

Best Video Finalists

• Magmites - Wireless Resonant Magnetic Microrobots by Dominic R. Frutiger, Bradley Kratochvil, Karl Vollmers and Bradley J. Nelson
• The OmniTread OT-4 Serpentine Robot by Johann Borenstein and Adam Borrell
• Preliminary Report: Rescue Robot at Crandall Canyon, Utah, Mine Disaster by Robin R. Murphy, Jeffery Kravitz, Ken Peligren, James Milward and Jeff Stanway

KUKA Service Robotics Best Paper Finalists

• Efficient Airport Snow Shoveling by Applying Autonomous Multi-Vehicle Formations by Martin Saska, Martin Hess and Klaus Schilling
• Hybrid Laser and Vision Based Object Search and Localization by Dorian Galvez Lopez, Kristoffer Sjo, Chandana Paul and Patric Jensfelt (Andi)
• Towards a Personal Robotics Development Platform: Rationale and Design of an Intrinsically Safe Personal Robot by Keenan A. Wyrobek, Eric H. Berger, H.F. Machiel Van der Loos, and J. Kenneth Salisbury (Jeff)
• VSA-II: A Novel Prototype of Variable Stiffness Actuator for Safe and Performing Robots Interacting with Humans by Riccardo Schiavi, Giorgio Grioli, Soumen Sen and Antonio Bicchi (Yu-Chun)

[Lab Meeting] May 12th, 2008 (Andi) Progress Report on 3D mapping

I will show some early results, and discuss several issues that still have to be addressed.

[Lab Meeting] May 12th, 2008 (Ekker): On foot navigation : continuous step calibration using both complementary recursive prediction and adaptive Kalm

Title:
On foot navigation : continuous step calibration
using both complementary recursive prediction
and adaptive Kalman filtering
From : ION 2000
Abstract:
Dead reckoning for on-foot navigation applications cannot
be computed by double integration of the antero-posterior
acceleration. The main reasons are the alignment problem
and the important sensor systematic errors in comparison
to human walking speed. However, raw accelerometer
signal can furnish helpful information on steps length as a
function of the walk dynamics. As stride length naturally
varies, a continuous adaptation is necessary. In the
absence of satellite observable, a recursive prediction
process is used. When GPS signal is available, adaptive
Kalman filtering is processed to update both the stride
length and the recursive prediction parameters. This paper
shows the different necessary stages for individual stride
calibration as basis of global on-foot dead reckoning
applications. This study lies within the framework of a
project that aims at analyzing the daily activity of people.
Precise continuous positioning, but not necessarily in real-
time conditions, appears of evident interest. The global
procedure and several test results are presented.
Link

Modeling and Visualizing the World from Internet Photo Collections

Noah Snavely
University of Washington

Tuesday May 13
10:00am NSH 3305

Abstract:
The Internet has become a massive source of photographic imagery. Billions of photos are available from sources ranging from Google Maps to Flickr, and myriad views of virtually every famous location on Earth are readily available. For instance, a Google Image search for "Eiffel Tower" returns almost half a million images, and a search for "Grand Canyon" returns nearly three million photos, representing many different photographers, viewpoints, times of day, weather conditions, and seasons. While extremely rich, these vast, unorganized photo collections are difficult to explore and search through using traditional photo browsing tools.

In this talk, I will present my work on new computer vision techniques for recovering the 3D structure of scenes from very large, diverse photo collections, and on new visualization techniques for exploring these reconstructed scenes in 3D. I will first describe Photo Tourism, an approach for navigating through photos using geometric controls. I will then discuss more recent work in creating simple, intuitive navigation interfaces by analyzing patterns in how people take photographs, and using these patterns to derive optimized 3D controls for each scene.

Bio:
Noah Snavely is a Ph.D. candidate in the Department of Computer Science and Engineering at the University of Washington, advised by Professor Steven Seitz and Dr. Richard Szeliski. His research interests span computer vision, computer graphics, and interactive techniques. He is particularly interested in developing new computer vision algorithms for the analysis of large, diverse photo collections, and on leveraging these algorithms to produce effective visualizations of scenes. He is the recipient of a National Science Foundation fellowship (2003) and a Microsoft Live Labs fellowship (2007).

Here is a link to a live demo of this work (and some videos)

Robotics Institute Thesis Proposal 12 May 2008

Online Adaptive Modeling for Outdoor Mobile Robots in Rough Terrain

Abstract:
Autonomous navigation by Unmanned Ground Vehicles (UGVs) in rough terrain is currently a problem of much interest and with many applications. ...
In this thesis, we propose a system for automatically identifying a vehicle model using conventional, on board state-estimation and terrain perception sensors. Such a system should be able automatically adapt to new or changing environments, vehicle damage or wear and tear. Research areas to be addressed include model structure, convergence and observability.

A copy of the thesis proposal document can be found at http://www.cs.cmu.edu/~dranders/fileserv/papers/ThesisProposal.pdf

[SCS Faculty Candidate Talk] Analyzing Dynamic Scenes from Moving Cameras: A Spacetime

SCS Faculty Candidate Yaser Sheikh
Wednesday, May 7th, 2008
10:00 a.m. NSH 3305
Host: Srinivasa Narasimhan, The Robotics Institute

Title:* *Analyzing Dynamic Scenes from Moving Cameras: A Spacetime Perspective

Abstract:
With the proliferation of camera-enabled cell phones, domestic robots, and wearable computers, moving cameras are being introduced /en masse/into society. The confluence of camera motion and the motion of objects in the scene complicates the task of understanding the scene from video. In this talk, I discuss how and when it is possible to disambiguatet hese two sources of motion, towards the goal of analyzing dynamic scenes from moving cameras.

I begin by considering a single camera viewing a dynamic scene. Unlike contemporary approaches to this problem, which try to model the variation in the shape of objects, I show that modeling the variation of points along time is better motivated physically and produces more stable reconstructions. This model also intuitively characterizes the inherent reconstruction ambiguity for a single camera and motivates the study of dynamic scenes from /multiple/ moving cameras. I present the case for conducting this analysis in spacetime, where a dynamic scene is considered a body in spacetime, and each video a spacetime image of this body. Through this representation, I demonstrate that classic algorithmsin multiview geometry that deal with static scenes can be lifted to spacetime, and applied directly for dynamic scene analysis. The analogues of factorization approaches and the fundamental matrix are described, leading to new, intuitive, relationships between the epipolar geometries of perspective images, linear pushbroom images and epipolar plane images.

[Lab Meeting] May 5th, 2008 (Kuo-Hwei), Progress Report on Laser-Based SLAMMOT

I will show the improvement of the multi-hypotheses SLAMMOT, and explain some detail of my implementation.

[Lab Meeting] May 5th, 2008 (Hero): An Application of Reinforcement Learning to Aerobatic Helicopter Flight

Pieter Abbeel, Adam Coates, Morgan Quigley, Andrew Y. Ng
Computer Science Dept.
Stanford University
Stanford, CA 94305

Abstract
Autonomous helicopter flight is widely regarded to be a highly challenging control
problem. This paper presents the first successful autonomous completion on a
real RC helicopter of the following four aerobatic maneuvers: forward flip and
sideways roll at low speed, tail-in funnel, and nose-in funnel. Our experimental
results significantly extend the state of the art in autonomous helicopter flight.
We used the following approach: First we had a pilot fly the helicopter to help
us find a helicopter dynamics model and a reward (cost) function. Then we used
a reinforcement learning (optimal control) algorithm to find a controller that is
optimized for the resulting model and reward function. More specifically, we used
differential dynamic programming (DDP), an extension of the linear quadratic
regulator (LQR).

link: http://www.cs.stanford.edu/%7Epabbeel/pubs/AbbeelCoatesQuigleyNg_aaorltahf_nips2006.pdf

[Lab Meeting] May 5th, 2008 (Stanley): Boosting Structured Prediction for Imitation Learning

Author:
Nathan Ratliff, David Bradley, J. Andrew Bagnell, Joel Chestnutt
Robotics Institute, Carnegie Mellon University

From:
Advances in Neural Information Processing Systems 19, MIT Press, Cambridge, MA, 2007

Abstract:
The Maximum Margin Planning (MMP) (Ratliff et al., 2006) algorithm solves imitation learning problems by learning linear mappings from features to cost functions in a planning domain. The learned policy is the result of minimum-cost planning using these cost functions. These mappings are chosen so that example policies (or trajectories) given by a teacher appear to be lower cost (with a loss-scaled margin) than any other policy for a given planning domain. We provide a novel approach, MMPBOOST , based on the functional gradient descent view of boosting (Mason et al., 1999; Friedman, 1999a) that extends MMP by “boosting”in new features. This approach uses simple binary classification or regression to improve performance of MMP imitation learning, and naturally extends to the class of structured maximum margin prediction problems. (Taskar et al., 2005) Our technique is applied to navigation and planning problems for outdoor mobile robots and robotic legged locomotion.

Link

MIT CSAIL talk : Predicting Listener Backchannel: A Probabilistic Multimodal Approach

Speaker: Louis-Philippe Morency, Research Scientist, Institute for Create Technologies, University of Southern California
Date: Monday, May 5 2008
Time: 2:00PM to 3:00PM
Refreshments: 1:45PM
Location: 32-D507Host: C. Mario Christoudias, Gerald Dalley, MIT CSAIL
Contact: C. Mario Christoudias, Gerald Dalley, 3-4278, 3-6095, cmch@csail.mit.edu , dalleyg@mit.edu

During face-to-face interactions, listeners use backchannel feedback such as head nods as a signal to the speaker that the communication is working and that they should continue speaking. Predicting these backchannel opportunities is an important milestone for building engaging and natural virtual humans. In this talk I will show how sequential probabilistic models (e.g., Hidden Markov Models (HMMs) or Conditional Random Fields (CRFs)) can automatically learn from a database of human-to-human interactions to predict listener backchannels using the speaker multimodal output features (e.g., prosody, spoken words and eye gaze). The main challenges addressed in this talk are (1) automatic selection of the relevant features and (2) optimal feature representation for probabilistic models. For prediction of visual backchannel cues (i.e., head nods), our prediction model shows a statistically significant improvement over a previously published approach based on hand-crafted rules.

link

NPR Science Friday talks robots

NPR's Science Friday last week had a show called "Building a More Sociable Robot." Guests include Helen Greiner (chair and co-founder of iRobot), Peter McOwen (Queen Mary, University of London), Dean Kamen (inventor of the iBot, Segway, and founder of FIRST), and Grant Cox (member of FIRST champion team The Thunder Chickens). Greiner and McOwen talk about what average people expect out of robots in terms of interaction, the relationship between interactive technology, price, and consumer demand, and what the state of technology is to get robots interacting with the environment and with us in a "natural" way. Kamen and Cox, meanwhile, talk about the FIRST program, how it's encouraging people to follow science, engineering, and technology as careers, and why robotics is so effective in doing this.

the postcast and link to the article

Robotic wheelchair docks like a spaceship

Link

A laser-guided robot wheelchair that automatically docks with the user's vehicle and loads itself into the back could give disabled drivers more freedom.

Using the new system, the user opens the door of their van and presses a button to lower the front seat so they can climb in. A remote control is then used to drive the chair round to the back of the van.

From here on, a computer inside the vehicle takes over. Using radio signals and laser guidance, it positions the chair onto the forks of a lift that hauls the wheelchair on board, and closes the door (see video, right).

The process is reversed once the driver reaches their destination.

Reliable docking
Researchers from Lehigh University in Bethlehem, Pennsylvania, US, working with a company called Freedom Sciences of Philadelphia, have demonstrated the system using a retrofitted, commercially available motorised wheelchair and a standard Chrysler minivan.

The researchers had originally planned to let users dock the empty wheelchair onto the forklift themselves, using the remote control and a camera mounted on the van. But it proved too difficult to position the chair accurately on the lift.

"The real challenge is to dock with 100% reliability. That is something you can't do with remote control," says John Spletzer, a roboticist at Lehigh who helped develop the system.

Instead they developed an on-board computer that uses a LIDAR (light detecting and ranging) system to position the chair. It bounces laser light off two reflectors on the armrests of the chair to track its position and align it with the forklift.

Space docking
Similar laser ranging was used by the uncrewed cargo spacecraft Jules Verne when it first docked with the International Space Station last month.

In tests, the system achieved a 97.5% success rate in docking the chair, even when facing complications such as rain, headlights, visible exhaust fumes, or loose gravel under the wheels.

If a docking attempt fails, the operator repositions the chair and tries again. If all else fails, they can take over, and keep trying until docking is successful.

Freedom Sciences expects to begin selling the system later this year in the US for around $30,000 each.

FDA approval
The price is comparable to having a vehicle modified so that a wheelchair can roll into the driver's area, and has the benefit that the equipment can be transferred when a new car is purchased, says Thomas Panzarella, Freedom Sciences' chief technology officer.

Because the product involves modifying a medical device – a wheelchair – approval is needed from the US Food and Drug Administration, but Panzarella expects this to take no more than a month or so.

The system tackles an interesting problem, says Joelle Pineau, a computer scientist at McGill University in Montreal. "It is well known that navigating in very constrained spaces and conditions is a major challenge for wheelchair users."

Journal reference: Journal of Field Robotics (DOI: 10.1002/rob.20236)