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

Lab Meeting March 16th, 2011 (Andi): 3D Deformable Face Tracking with a Commodity Depth Camera

Qin Cai , David Gallup , Cha Zhang and Zhengyou Zhang

Abstract: Recently, there has been an increasing number of depth cameras available at commodity prices. These cameras can usually capture both color and depth images in real-time, with limited resolution and accuracy. In this paper, we study the problem of 3D deformable face tracking with such commodity depth cameras. A regularized maximum

likelihood deformable model fitting (DMF) algorithm is developed, with special emphasis on handling the noisy input depth data. In particular, we present a maximum likelihood solution that can accommodate sensor noise represented by an arbitrary covariance matrix, which allows more elaborate modeling of the sensor’s accuracy. Furthermore, an 1 regularization scheme is proposed based on the semantics of the deformable face model, which is shown to be very effective in improving the tracking results. To track facial movement in subsequent frames, feature points in the texture images are matched across frames and integrated into the DMF framework seamlessly. The effectiveness of the proposed method is demonstrated with multiple sequences with ground truth information.

Full Paper

Lab Meeting March 9th, 2011(KuoHuei): progress report

I will present my progress on Neighboring Objects Interaction models and tracking system.

Lab Meeting March 9, 2011 (Wang Li): Real-time Identification and Localization of Body Parts from Depth Images (ICRA 2010)

Real-time Identification and Localization of Body Parts from Depth Images

Christian Plagemann
Varun Ganapathi
Daphne Koller
Sebastian Thrun

Abstract

We deal with the problem of detecting and identifying body parts in depth images at video frame rates. Our solution involves a novel interest point detector for mesh and range data that is particularly well suited for analyzing human shape. The interest points, which are based on identifying geodesic extrema on the surface mesh, coincide with salient points of the body, which can be classified using local shape descriptors. Our approach also provides a natural way of estimating a 3D orientation vector for a given interest point. This can be used to normalize the local shape descriptors to simplify the classification problem as well as to directly estimate the orientation of body parts in space.
Experiments show that our interest points in conjunction with a boosted patch classifier are significantly better in detecting body parts in depth images than state-of-the-art sliding-window based detectors.

Paper Link

Article: Perception beyond the Here and Now

by Albrecht Schmidt, Marc Langheinrich, and Kristian Kersting
Computer, February 2011, pp. 86–88

A multitude of senses provide us with information about the here and now. What we see, hear, and feel in turn shape how we perceive our surroundings and understand the world. Our senses are extremely limited, however, and ever since humans began creating and using technology, they have tried to enhance their natural perception in various ways. (pdf)

Lab Meeting March 2nd, 2011 (Jeff): Observability-based Rules for Designing Consistent EKF SLAM Estimators

Title: Observability-based Rules for Designing Consistent EKF SLAM Estimators

Authors: Guoquan P. Huang, Anastasios Mourikis, and Stergios I. Roumeliotis

Abstract:

In this work, we study the inconsistency problem of extended Kalman filter (EKF)-based simultaneous localization and mapping (SLAM) from the perspective of observability. We analytically prove that when the Jacobians of the process and measurement models are evaluated at the latest state estimates during every time step, the linearized error-state system employed in the EKF has an observable subspace of dimension higher than that of the actual, non-linear, SLAM system. As a result, the covariance estimates of the EKF undergo reduction in
directions of the state space where no information is available, which is a primary cause of the inconsistency. Based on these theoretical results, we propose a general framework for improving the consistency of EKF-based SLAM. In this framework, the EKF linearization points are selected in a way that ensures that the resulting linearized system model has an observable subspace of appropriate dimension. We describe two algorithms that are instances of this paradigm. In the first, termed observability constrained (OC)-EKF, the linearization points are selected so as to minimize their expected errors (i.e. the difference between the linearization point and the true state) under the observability constraints. In the second, the filter Jacobians are calculated using the first-ever available estimates for all state variables. This latter approach is termed first-estimates Jacobian (FEJ)-EKF. The proposed algorithms have been tested both in simulation and experimentally, and are shown to significantly outperform the standard EKF both in terms of accuracy and consistency.

Link:
The International Journal of Robotics Research(IJRR), Vol.5 April 2010
http://ijr.sagepub.com/content/29/5/502.full.pdf+html

Meka's M-1 Mobile Manipulator

Meka M1 Mobile Manipulator vimeo from Meka Robotics on Vimeo.

Lab Meeting February 14, 2011 (fish60): Feature Construction for Inverse Reinforcement Learning

Title: Feature Construction for Inverse Reinforcement Learning
Sergey Levine, Zoran Popović, Vladlen Koltun
NIPS 2010

Abstract:
The goal of inverse reinforcement learning is to find a reward function for a
Markov decision process, given example traces from its optimal policy. Current
IRL techniques generally rely on user-supplied features that form a concise basis
for the reward. We present an algorithm that instead constructs reward features
from a large collection of component features, by building logical conjunctions of
those component features that are relevant to the example policy. Given example
traces, the algorithm returns a reward function as well as the constructed features.

Link