A robot that flies like a bird | Video on TED.com

A robot that flies like a bird | Video on TED.com

Lab meeting June 29th (Jim): A Reduction of Imitation Learning and Structured Prediction to No-Regret Online Learning

Title: A Reduction of Imitation Learning and Structured Prediction to No-Regret Online Learning

Stephane Ross, Geoffrey Gordon, and J. Andrew (Drew) Bagnell

Proceedings of the 14th International Conference on Artificial Intelligence and Statistics (AISTATS), April, 2011.

Abstracts:
Sequential prediction problems such as imitation learning, where future observations depend on previous predictions (actions), violate the common i.i.d. assumptions made in statistical learning. ... In this paper, we propose a new iterative algorithm, which trains a stationary deterministic policy, that can be seen as a no regret algorithm in an online learning setting. We show that any such no regret algorithm, combined with additional reduction assumptions, must find a policy with good performance under the distribution of observations it induces in such sequential settings.

Link

Lab Meeting June 22th (Chih-Chung):Minimum Snap Trajectory Generation and Control for Quadrotors (ICRA2011,best paper)

Title: Minimum Snap Trajectory Generation and Control for Quadrotors

Authors: Daniel Mellinger and Vijay Kumar

Abstracts:
We address the controller design and the trajectory
generation for a quadrotor maneuvering in three
dimensions in a tightly constrained setting typical of indoor
environments. In such settings, it is necessary to allow for
significant excursions of the attitude from the hover state and
small angle approximations cannot be justified for the roll
and pitch. We develop an algorithm that enables the real-time
generation of optimal trajectories through a sequence of 3-D
positions and yaw angles, while ensuring safe passage through
specified corridors and satisfying constraints on velocities,
accelerations and inputs. A nonlinear controller ensures the
faithful tracking of these trajectories. Experimental results
illustrate the application of the method to fast motion (5-10
body lengths/second) in three-dimensional slalom courses.

[link]

Lab Meeting June 15th (Shao-Chen): Distributed Robust Data Fusion Based on Dynamic Voting (ICRA2011)

Title: Distributed Robust Data Fusion Based on Dynamic Voting

Authors: Eduardo Montijano, Sonia Mart´ınez and Carlos Sagues

Abstract:

Data association mistakes, estimation and measurement errors are some of the factors that can contribute to incorrect observations in robotic sensor networks. In order to act reliably, a robotic network must be able to fuse and correct its perception of the world by discarding any outlier information. This is a difficult task if the network is to be deployed remotely and the robots do not have access to groundtruth sites or manual calibration. In this paper, we present a novel, distributed scheme for robust data fusion in autonomous robotic networks. The proposed method adapts the RANSAC algorithm to exploit measurement redundancy, and enables robots determine an inlier observation with local communications. Different hypotheses are generated and voted for using a dynamic consensus algorithm. As the hypotheses are computed, the robots can change their opinion making the voting process dynamic. Assuming that at least one hypothesis is initialized with only inliers, we show that the method converges to the maximum likelihood of all the inlier observations in a general instance. Several simulations exhibit the good performance of the algorithm, which also gives acceptable results in situations where the conditions to guarantee convergence do not hold.

[link]

Lab Meeting June 15th (David): Sparse Scene Flow Segmentation for Moving Object Detection (Intelligent Vehicles Symposium 2011)

Title: Sparse Scene Flow Segmentation for Moving Object Detection (Intelligent Vehicles Symposium 2011)

Authors: P. Lenz, J. Ziegler, A. Geiger, M. Roser

Abstract:
Modern driver assistance systems such as collision avoidance or intersection assistance need reliable information on the current environment. Extracting such information from camera-based systems is a complex and challenging task for inner city taffic scenarios. This paper presents an approach for object detection utilizing sparse scene flow. For consecutive stereo images taken from a moving vehicle, corresponding interest points are extracted. Thus, for every interest point, disparity and optical flow values are known and consequently, scene flow can be calculated. Adjacent interest points describing a similar scene flow are considered to belong to one rigid object. The proposed method does not rely on object classes and allows for a robust detection of dynamic objects in traffic scenes. Leading vehicles are continuously detected for several frames. Oncoming objects are detected within five frames after their appearance.

Link: http://www.rainsoft.de/publications/iv11b.pdf

Lab Meeting June 8th, 2011 (Jeff): Incremental Construction of the Saturated-GVG for Multi-Hypothesis Topological SLAM

Title: Incremental Construction of the Saturated-GVG for Multi-Hypothesis
Topological SLAM

Authors: Tong Tao, Stephen Tully, George Kantor, and Howie Choset

Abstract:

The generalized Voronoi graph (GVG) is a topological representation of an environment that can be incrementally constructed with a mobile robot using sensor-based control. However, because of sensor range limitations, the GVG control law will fail when the robot moves into a large open area. This paper discusses an extended GVG approach to topological navigation and mapping: the saturated generalized Voronoi graph (S-GVG), for which the robot employs an additional wall-following behavior to navigate along obstacles at the range limit of the sensor. In this paper, we build upon previous work related to the S-GVG and provide two important contributions: 1) a rigorous discussion of the control laws and algorithm modifications that are necessary for incremental construction of the S-GVG with a mobile robot, and 2) a method for incorporating the S-GVG into a novel multi-hypothesis SLAM algorithm for loop-closing and localization. Experiments with a wheeled mobile robot in an office-like environment validate the ffectiveness of the proposed approach.

Link:
IEEE International Conference on Robotics and Automation(ICRA), 2011
http://www.cs.cmu.edu/~biorobotics/papers/icra11_tao.pdf
LocalLink

Lab Meeting June 1, 2011 (Alan): Semantic Structure from Motion (CVPR 2011)

Title: Semantic Structure from Motion (CVPR 2011)

Authors: Sid Yingze Bao and Silvio Savarese

Abstract

Conventional rigid structure from motion (SFM) addresses the problem of recovering the camera parameters (motion) and the 3D locations (structure) of scene points, given observed 2D image feature points. In this paper, we propose a new formulation called Semantic Structure From Motion (SSFM). In addition to the geometrical constraints provided by SFM, SSFM takes advantage of both semantic and geometrical properties associated with objects in the scene (Fig. 1). These properties allow us to recover not only the structure and motion but also the 3D locations, poses, and categories of objects in the scene. We cast this problem as a max-likelihood problem where geometry (cameras, points, objects) and semantic information (object classes) are simultaneously estimated. The key intuition is that, in addition to image features, the measurements of objects across views provide additional geometrical constraints that relate cameras and scene parameters. These constraints make the geometry estimation process more robust and, in turn, make object detection more accurate. Our framework has the unique ability to: i) estimate camera poses only from object detections, ii) enhance camera pose estimation, compared to feature-point-based SFM algorithms, iii) improve object detections given multiple uncalibrated images, compared to independently detecting objects in single images. Extensive quantitative results on three datasets – LiDAR cars, street-view pedestrians, and Kinect office desktop – verify our theoretical claims.

Link

Lab Meeting June 1, 2011 (Wang Li): Articulated pose estimation with flexible mixtures-of-parts (CVPR 2011)

Articulated pose estimation with flexible mixtures-of-parts

Yi Yang
Deva Ramanan

Abstract

We describe a method for human pose estimation in static images based on a novel representation of part models. Notably, we do not use articulated limb parts, but rather capture orientation with a mixture of templates for each part. We describe a general, flexible mixture model for capturing contextual co-occurrence relations between parts, augmenting standard spring models that encode spatial relations. We show that such relations can capture notions of local rigidity. When co-occurrence and spatial relations are tree-structured, our model can be efficiently optimized with dynamic programming. We present experimental results on standard benchmarks for pose estimation that indicate our approach is the state-of-the-art system for pose estimation, outperforming past work by 50% while being orders of magnitude faster.

Paper Link

ICRA 2011 Awards

Best Manipulation Paper

• WINNER! Characterization of Oscillating Nano Knife for Single Cell Cutting by Nanorobotic Manipulation System Inside ESEM: Yajing Shen, Masahiro Nakajima, Seiji Kojima, Michio Homma, Yasuhito Ode, Toshio Fukuda [pdf]
• Wireless Manipulation of Single Cells Using Magnetic Microtransporters: Mahmut Selman Sakar, Edward Steager, Anthony Cowley, Vijay Kumar, George J Pappas
• Hierarchical Planning in the Now: Leslie Kaelbling, Tomas Lozano-Perez
• Selective Injection and Laser Manipulation of Nanotool Inside a Specific Cell Using Optical Ph Regulation and Optical Tweezers: Hisataka Maruyama, Naoya Inoue, Taisuke Masuda, Fumihito Arai
• Configuration-Based Optimization for Six Degree-Of-Freedom Haptic Rendering for Fine Manipulation: Dangxiao Wang, Xin Zhang, Yuru Zhang, Jing Xiao

Best Vision Paper

• Model-Based Localization of Intraocular Microrobots for Wireless Electromagnetic Control: Christos Bergeles, Bradley Kratochvil, Bradley J. Nelson
• Fusing Optical Flow and Stereo in a Spherical Depth Panorama Using a Single-Camera Folded Catadioptric Rig: Igor Labutov, Carlos Jaramillo, Jizhong Xiao
• 3-D Scene Analysis Via Sequenced Predictions Over Points and Regions: Xuehan Xiong, Daniel Munoz, James Bagnell, Martial Hebert
• Fast and Accurate Computation of Surface Normals from Range Images: Hernan Badino, Daniel Huber, Yongwoon Park, Takeo Kanade
• WINNER! Sparse Distance Learning for Object Recognition Combining RGB and Depth Information: Kevin Lai, Liefeng Bo, Xiaofeng Ren, Dieter Fox [pdf]

Best Automation Paper

• WINNER! Automated Cell Manipulation: Robotic ICSI: Zhe Lu, Xuping Zhang, Clement Leung, Navid Esfandiari, Robert Casper, Yu Sun [pdf]
• Efficient AUV Navigation Fusing Acoustic Ranging and Side-Scan Sonar: Maurice Fallon, Michael Kaess, Hordur Johannsson, John Leonard
• Vision-Based 3D Bicycle Tracking Using Deformable Part Model and Interacting Multiple Model Filter: Hyunggi Cho, Paul E. Rybski, Wende Zhang
• High-Accuracy GPS and GLONASS Positioning by Multipath Mitigation Using Omnidirectional Infrared Camera: Taro Suzuki, Mitsunori Kitamura, Yoshiharu Amano, Takumi Hashizume
• Deployment of a Point and Line Feature Localization System for an Outdoor Agriculture Vehicle: Jacqueline Libby, George Kantor

Best Medical Robotics Paper

• Design of Adjustable Constant-Force Forceps for Robot-Assisted Surgical Manipulation: Chao-Chieh Lan, Jung-Yuan Wang
• Design Optimization of Concentric Tube Robots Based on Task and Anatomical Constraints: Chris Bedell, Jesse Lock, Andrew Gosline, Pierre Dupont
• GyroLock - First in Vivo Experiments of Active Heart Stabilization Using Control Moment Gyro (CMG): Julien Gagne, Olivier Piccin, Edouard Laroche, Michele Diana, Jacques Gangloff
• Metal MEMS Tools for Beating-Heart Tissue Approximation: Evan Butler, Chris Folk, Adam Cohen, Nikolay Vasilyev, Rich Chen, Pedro del Nido, Pierre Dupont
• WINNER! An Articulated Universal Joint Based Flexible Access Robot for Minimally Invasive Surgery: Jianzhong Shang, David Noonan, Christopher Payne, James Clark, Mikael Hans Sodergren, Ara Darzi, Guang-Zhong Yang [pdf]

Best Conference Paper

• WINNER! Minimum Snap Trajectory Generation and Control for Quadrotors: Daniel Mellinger, Vijay Kumar [pdf]
• Autonomous Multi-Floor Indoor Navigation with a Computationally Constrained Micro Aerial Vehicle: Shaojie Shen, Nathan Michael, Vijay Kumar
• Dexhand : A Space Qualfied Multi-Fingered Robotic Hand: Maxime Chalon, Armin Wedler, Andreas Baumann, Wieland Bertleff, Alexander Beyer, Jörg Butterfass, Markus Grebenstein, Robin Gruber, Franz Hacker, Erich Krämer, Klaus Landzettel, Maximilian Maier, Hans-Juergen Sedlmayr, Nikolaus Seitz, Fabian Wappler, Bertram Willberg, Thomas Wimboeck, Frederic Didot, Gerd Hirzinger
• Time Scales and Stability in Networked Multi-Robot Systems: Mac Schwager, Nathan Michael, Vijay Kumar, Daniela Rus
• Bootstrapping Bilinear Models of Robotic Sensorimotor Cascades: Andrea Censi, Richard Murray

KUKA Service Robotics Best Paper

• Distributed Coordination and Data Fusion for Underwater Search: Geoffrey Hollinger, Srinivas Yerramalli, Sanjiv Singh, Urbashi Mitra, Gaurav Sukhatme
• WINNER! Dynamic Shared Control for Human-Wheelchair Cooperation: Qinan Li, Weidong Chen, Jingchuan Wang [pdf]
• Towards Joint Attention for a Domestic Service Robot -- Person Awareness and Gesture Recognition Using Time-Of-Flight Cameras: David Droeschel, Jorg Stuckler, Dirk Holz, Sven Behnke
• Electromyographic Evaluation of Therapeutic Massage Effect Using Multi-Finger Robot Hand: Ren C. Luo, Chih-Chia Chang

Best Video

• Catching Flying Balls and Preparing Coffee: Humanoid Rollin'Justin Performs Dynamic and Sensitive Tasks: Berthold Baeuml, Florian Schmidt, Thomas Wimboeck, Oliver Birbach, Alexander Dietrich, Matthias Fuchs, Werner Friedl, Udo Frese, Christoph Borst, Markus Grebenstein, Oliver Eiberger, Gerd Hirzinger
• Recent Advances in Quadrotor Capabilities: Daniel Mellinger, Nathan Michael, Michael Shomin, Vijay Kumar
• WINNER! High Performance of Magnetically Driven Microtools with Ultrasonic Vibration for Biomedical Innovations: Masaya Hagiwara, Tomohiro Kawahara, Lin Feng, Yoko Yamanishi, Fumihito Arai [pdf]

Best Cognitive Robotics Paper

• WINNER! Donut As I Do: Learning from Failed Demonstrations: Daniel Grollman, Aude Billard [pdf]
• A Discrete Computational Model of Sensorimotor Contingencies for Object Perception and Control of Behavior: Alexander Maye, Andreas Karl Engel
• Skill Learning and Task Outcome Prediction for Manipulation: Peter Pastor, Mrinal Kalakrishnan, Sachin Chitta, Evangelos Theodorou, Stefan Schaal
• Integrating Visual Exploration and Visual Search in Robotic Visual Attention: The Role of Human-Robot Interaction: Momotaz Begum, Fakhri Karray

Lab Meeting May 3rd (Andi): Face/Off: Live Facial Puppetry

Thibaut Weise, Hao Li, Luc Van Gool, Mark Pauly

Proceedings of the Eighth ACM SIGGRAPH / Eurographics Symposium on Computer Animation 2009, Best Paper Award

We present a complete integrated system for live facial puppetry that enables high-resolution real-time facial expression tracking with transfer to another person's face. The system utilizes a real-time structured light scanner that provides dense 3D data and texture. A generic template mesh, fitted to a rigid reconstruction of the actor's face, is tracked offline in a training stage through a set of expression sequences. These sequences are used to build a person-specific linear face model that is subsequently used for online face tracking and expression transfer. Even with just a single rigid pose of the target face, convincing real-time facial animations are achievable. The actor becomes a puppeteer with complete and accurate control over a digital face.

[paper]

Lab Meeting May 3( KuenHan ), Multiple Targets Tracking in World Coordinate with a Single, Minimally Calibrated Camera (ECCV 2010)

Title: Multiple Targets Tracking in World Coordinate with a Single, Minimally Calibrated Camera. ( ECCV 2010, poster)
Author: Wongun Choi, Silvio Savarese.

Abstract:
Tracking multiple objects is important in many application
domains. We propose a novel algorithm for multi-object tracking that
is capable of working under very challenging conditions such as min-
imal hardware equipment, uncalibrated monocular camera, occlusions
and severe background clutter. To address this problem we propose a
new method that jointly estimates object tracks, estimates correspond-
ing 2D/3D temporal trajectories in the camera reference system as well
as estimates the model parameters (pose, focal length, etc) within a
coherent probabilistic formulation. Since our goal is to estimate stable
and robust tracks that can be univocally associated to the object IDs,
we propose to include in our formulation an interaction (attraction and
repulsion) model that is able to model multiple 2D/3D trajectories in
space-time and handle situations where objects occlude each other. We
use a MCMC particle ltering algorithm for parameter inference and
propose a solution that enables accurate and e cient tracking and cam-
era model estimation. Qualitative and quantitative experimental results
obtained using our own dataset and the publicly available ETH dataset
shows very promising tracking and camera estimation results.

Link
Website

NTU PAL Thesis Defense: Mobile Robot Localization in Large-scale Dynamic Environments

Mobile Robot Localization in Large-scale Dynamic Environments

Shao-Wen Yang

Doctoral Dissertation Defense

Department of Computer Science and Information Engineering

National Taiwan University

Time: Thursday, 19 May, 2011 at 8:00AM +0800 (CST)

Location: R542, Der-Tian Hall

Advisor: Chieh-Chih Wang

Thesis Committee:

Li-Chen Fu

Jane Yung-Jen Hsu

Han-Pang Huang

Ta-Te Lin

Chu-Song Chen, Sinica

Jwu-Sheng Hu, NCTU

John J. Leonard, MIT

Abstract:

Localization is the most fundamental problem to providing a mobile robot with autonomous capabilities. Whilst simultaneous localization and mapping (SLAM) and moving object tracking (MOT) have attracted immense attention in the last decade, the focus of robotics continues to shift from stationary robots in a factory automation environment to mobile robots operating in human-inhabited environments. State of the art relying on the static world assumption can fail in the real environment that is typically dynamic. Specifically, the real environment is challenging for mobile robots due to the variety of perceptual inconsistency over space and time. Development of situational awareness is particularly important so that the mobile robots can adapt quickly to changes in the environment.

In this thesis, we explore the problem of mobile robot localization in the real world in theory and practice, and show that localization can benefit from both stationary and dynamic entities.

The performance of ego-motion estimation depends on the consistency between sensory information at successive time steps, whereas the performance of localization relies on the consistency between the sensory information and the a priori map. The inconsistencies make a robot unable to robustly determine its location in the environment. We show that mobile robot localization, as well as ego-motion estimation, and moving object detection are mutually beneficial. Most importantly, addressing the inconsistencies serves as the basis for mobile robot localization, and forms a solid bridge between SLAM and MOT.

Localization, as well as moving object detection, is not only challenging but also difficult to evaluate quantitatively due to the lack of a realistic ground truth. As the key competencies for mobile robotic systems are localization and semantic context interpretation, an annotated data set, as well as an interactive annotation tool, is released to facilitate the development, evaluation and comparison of algorithms for localization, mapping, moving object detection, moving object tracking, etc.

In summary, a unified stochastic framework is introduced to solve the problems of motion estimation and motion segmentation simultaneously in highly dynamic environments in real time. A dual-model localization framework that uses information from both the static scene and dynamic entities is proposed to improve the localization performance by explicitly incorporating, rather than filtering out, moving object information. In the ample experiment, a sub-meter accuracy is achieved, without the aid of GPS, which is adequate for autonomous navigation in crowded urban scenes. The empirical results suggest that the performance of localization can be improved when handling the changing environment explicitly.

Download:

• Thesis draft: http://any.csie.ntu.edu.tw/thesis/yang_thesis-v1_0.pdf

Lab Meeting April 20, 2011 (fish60): Donut as I do: Learning from failed demonstrations

Title: Donut as I do: Learning from failed demonstrations In: 2011 IEEE International Conference on Robotics and Automation Authors: Grollman, Daniel (Ecole Polytechnique Federale de Lausanne), Billard, Aude (EPFL) Abstract The canonical Robot Learning from Demonstration scenario has a robot observing human demonstrations of a task or behavior in a few situations, and then developing a generalized controller. ... However, the underlying assumption is that the demonstrations are successful, and are appropriate to reproduce. We, instead, consider the possibility that the human has failed in their attempt, and their demonstration is an example of what not to do. Thus, instead of maximizing the similarity of generated behaviors to those of the demonstrators, we examine two methods that deliberately avoid repeating the human's mistakes. Link

Lab Meeting April 13, 2011 (Will): Hilbert Space Embeddings of Hidden Markov Models (ICML2010)

Titile: Hilbert Space Embeddings of Hidden Markov Model

In: ICML 2010

Authors: Le Song, Byron Boots, Sajid Siddiqi, Geoffrey Gordon, Alex Smola

Abstract

Hidden Markov Models (HMMs) are important tools for modeling sequence data. However, they are restricted to discrete latent states, and are largely restricted to Gaussian and discrete observations. And, learning algorithms for HMMs have predominantly relied on local search heuristics, with the exception of spectral methods such as those described below. We propose a nonparametric HMM that extends traditional HMMs to structured and non-Gaussian continuous distributions. Furthermore, we derive a local-minimum-free kernel spectral algorithm for learning these HMMs. We apply our method to robot vision data, slot car inertial sensor data and audio event classification data, and show that in these applications, embedded HMMs exceed the previous state-of-the-art performance.

[pdf]

Lab Meeting April 13, 2011 (Jimmy): WiFi-SLAM Using Gaussian Process Latent Variable Models (IJCAI2007)

Title: WiFi-SLAM Using Gaussian Process Latent Variable Models
In: IJCAI 2007
Authors: Brian Ferris, Dieter Fox, and Neil Lawrence

Abstract
WiFi localization, the task of determining the physical location of a mobile device from wireless signal strengths, has been shown to be an accurate method of indoor and outdoor localization and a powerful building block for location-aware applications. However, most localization techniques require a training set of signal strength readings labeled against a ground truth location map, which is prohibitive to collect and maintain as maps grow large. In this paper we propose a novel technique for solving the WiFi SLAM problem using the Gaussian Process Latent Variable Model (GPLVM) to determine the latent-space locations of unlabeled signal strength data. We show how GPLVM, in combination with an appropriate motion dynamics model, can be used to reconstruct a topological connectivity graph from a signal strength sequence which, in combination with the learned Gaussian Process signal strength model, can be used to perform efficient localization.

[pdf]

Lab Meeting March 30, 2011 (Chih-Chung): Progress Report

I will show my recent work of moving target tracking and following, using laser scanner and PIONEER3 robot.

Lab Meeting March 30, 2011 (Chung-Han): Progress Report

I will show the updated ground-truth annotation system with the newly collected data set.

Lab Meeting March 23, 2011 (David): Object detection and tracking for autonomous navigation in dynamic environments (IJRR 2010)

Title: Object detection and tracking for autonomous navigation in dynamic environments (IJRR 2010)

Authors: Andreas Ess, Konrad Schindler, Bastian Leibe, Luc Van Gool

Abstract:
We address the problem of vision-based navigation in busy inner-city locations, using a stereo rig mounted on a mobile platform. In this scenario semantic information becomes important: rather than modeling moving objects as arbitrary obstacles, they should be categorized and tracked in order to predict their future behavior. To this end, we combine classical geometric world mapping with object category detection and tracking. Object-category-specific detectors serve to find instances of the most important object classes (in our case pedestrians and cars). Based on these detections, multi-object tracking recovers the objects' trajectories, thereby making it possible to predict their future locations, and to employ dynamic path planning. The approach is evaluated on challenging, realistic video sequences recorded at busy inner-city locations.

Link

Lab Meeting March 23, 2011 (Shao-Chen): A Comparison of Track-to-Track Fusion Algorithms for Automotive Sensor Fusion (MFI2008)

Title: A Comparison of Track-to-Track Fusion Algorithms for Automotive Sensor Fusion (MFI2008, Multisensor Fusion and Integration for Intelligent Systems)

Authors: Stephan Matzka and Richard Altendorfer

Abstract:

In  exteroceptive  automotive  sensor  fusion,  sensor data are usually only available as processed, tracked object data and not as  raw sensor data. Applying a Kalman filter  to such data  leads  to  additional  delays  and  generally  underestimates the  fused  objects'  covariance  due  to temporal  correlations  of individual  sensor  data  as  well  as inter-sensor  correlations.  We compare the performance of a standard asynchronous Kalman filter applied to tracked sensor data to several algorithms for the track-to-track fusion  of sensor objects of unknown  correlation, namely covariance  union,  covariance  intersection,  and  use  of cross-covariance.  For  the  simulation  setup  used  in  this  paper, covariance  intersection  and  use  of  cross-covariance  turn  out to  yield  significantly  lower  errors  than  a  Kalman  filter  at  a comparable computational load.

Link