Lab Meeting, March 28, 2013 (Chiang Yi): Efficient Model-based 3D Tracking of Hand Articulations using Kinect (BMVC 2011)

Authors: Iason Oikonomidis, Nikolaos Kyriazis
 ,Antonis A. Argyros

Abstract:
We present a novel solution to the problem of recovering and tracking the 3D po-
sition, orientation and full articulation of a human hand from markerless visual obser-
vations obtained by a Kinect sensor. We treat this as an optimization problem, seeking
for the hand model parameters that minimize the discrepancy between the appearance
and 3D structure of hypothesized instances of a hand model and actual hand observa-
tions. This optimization problem is effectively solved using a variant of Particle Swarm
Optimization (PSO). The proposed method does not require special markers and/or a
complex image acquisition setup. Being model based, it provides continuous solutions
to the problem of tracking hand articulations. Extensive experiments with a prototype
GPU-based implementation of the proposed method demonstrate that accurate and ro-
bust 3D tracking of hand articulations can be achieved in near real-time (15Hz).


LINK

extended work: Tracking the articulated motion of two strongly interacting hands

Lab Meeting, March 21, 2013 (Yen-Ting): Extracting 3D Scene-Consistent Object Proposals and Depth from Stereo Images (ECCV 2012)

Authors: Michael Bleyer, Christoph Rhemann, and Carsten Rother

Abstract: This work combines two active areas of research in computer vision: unsupervised object extraction from a single image, and depth estimation from a stereo image pair. A recent, successful trend in unsupervised object extraction is to exploit so-called “3D scene-consistency”, that is enforcing that objects obey underlying physical constraints of the 3D scene, such as occupancy of 3D space and gravity of objects. Our main contribution is to introduce the concept of 3D scene-consistency into stereo matching. We show that this concept is beneficial for both tasks, object extraction and depth estimation. In particular, we demonstrate that our approach is able to create a large set of 3D scene-consistent object proposals, by varying e.g. the prior on the number of objects...

Link

Lab Meeting, March 14, 2013 (Channing): The Design of LEO: a 2D Bipedal Walking Robot for Online Autonomous Reinforcement Learning (IROS 2010)

Authors: Erik Schuitema, Martijn Wisse, Thijs Ramakers and Pieter Jonker

Abstract: Real robots demonstrating online Reinforcement Learning (RL) to learn new tasks are hard to find. The specific properties and limitations of real robots have a large impact on their suitability for RL experiments. In this work, we derive the main hardware and software requirements that a RL robot should fulfill, and present our biped robot LEO that was specifically designed to meet these requirements. We verify its aptitude in autonomous walking experiments using a pre-programmed controller. Although there is room
for improvement in the design, the robot was able to walk, fall and stand up without human intervention for 8 hours, during which it made over 43,000 footsteps.

Link

Lab Meeting, March 7, 2013 (Benny):A Segmentation and Data Association Annotation System for Laser-based Multi-Target Tracking Evaluation

Author: Chien-Chen Weng, Chieh-Chih Wang and Jennifer Healey

Abstract—2D laser scanners are now widely used to accomplish robot perception tasks such as SLAM and multi-target tracking (MTT). While a number of SLAM benchmarking datasets are available, only a few works have discussed the issues of collecting multi-target tracking benchmarking datasets.

In this work, a segmentation and data association annotation system is proposed for evaluating multi-target tracking using 2D laser scanners. The proposed annotation system uses the existing MTT algorithm to generate initial annotation results and uses camera images as the strong hints to assist annotators to recognize moving objects in laser scans. The annotators can draw the object’s shape and future trajectory to automate segmentation and data association and reduce the annotation task loading. The user study results show that the performance of the proposed annotation system is superior in the V-measure vs. annotation speed tests and the false positive and false negative rates.

Link

Lab meeting Feb. 21, 2013 (ChihChung) A Tensor-Based Algorithm for High-Order Graph Matching (PAMI 2010)

Authors: Olivier Duchenne, Francis Bach, In-So Kweon, and Jean Ponce

Abstract: This paper addresses the problem of establishing correspondences between two sets of visual features using higher-order constraints instead of the unary or pairwise ones used in classical methods. Concretely, the corresponding hypergraph matching problem is formulated as the maximization of a multi-linear objective function over all permutations of the features. This function is defined by a tensor representing the affinity between feature tuples. It is maximized using a generalization of spectral techniques where a relaxed problem is first solved by a multi-dimensional power method, and the solution is then projected onto the closest assignment matrix. The proposed approach has been implemented, and it is compared to state-of-the-art algorithms on both synthetic and real data.

Link

Lab meeting Jan. 23, 2013 (Gene): Fully Distributed Scalable Smoothing and Mapping with Robust Multi-robot Data Association (IEEE 2012)

Title: Fully Distributed Scalable Smoothing and Mapping with Robust Multi-robot Data Association (IEEE 2012)
Authors: Alexander Chunningham, Kai M. Wurm, Wolfarm Burgard, and Frank Dellaert

Abstract:


In this paper we focus on the multi-robot perception problem, and present an experimentally validated end-to-end multi-robot mapping framework, enabling individual robots in a team to see beyond their individual sensor horizons. The inference part of our system is the DDF-SAM algorithm [1], which provides a decentralized communication and inference scheme, but did not address the crucial issue of data association.

One key contribution is a novel, RANSAC-based, approach for performing the between-robot data associations and initialization of relative frames of reference. We demonstrate this system with both data collected from real robot experiments, as well as in a large scale simulated experiment demonstrating the scalability of the proposed approach.

Link

Lab meeting Jan 9th 2013 (Bang-Cheng Wang): Kicking a Ball – Modeling Complex Dynamic Motions for Humanoid Robots

Presented by Bang-Cheng Wang

From RoboCup 2010: Robot Soccer World Cup XIV, ser. Lecture Notes 

in Artificial Intelligence, E. Chown, A. Matsumoto, P. Pl¨oger, 

and J. R. del Solar, Eds. Springer, to appear in 2011.

Authors:
Judith Müller, Tim Laue, and Thomas Röfer

Abstract:
Complex motions like kicking a ball into the goal are becoming
more important in RoboCup leagues such as the Standard Platform
League. Thus, there is a need for motion sequences that can be parameterized
and changed dynamically. This paper presents a motion engine
that translates motions into joint angles by using trajectories. These
motions are defined as a set of Bezier curves that can be changed online
to allow adjusting, for example, a kicking motion precisely to the actual
position of the ball. During the execution, motions are stabilized by
the combination of center of mass balancing and a gyro feedback-based
closed-loop PID controller.

Lab Meeting December 19th, 2012 (Jeff): Inference on networks of mixtures for robust robot mapping

Title: Inference on networks of mixtures for robust robot mapping

Authors: Edwin Olson and Pratik Agarwal

Abstract:

The central challenge in robotic mapping is obtaining reliable data associations (or “loop closures”): state-of-the-art inference algorithms can fail catastrophically if even
one erroneous loop closure is incorporated into the map. Consequently, much work has been done to push error rates closer to zero. However, a long-lived or multi-robot system will
still encounter errors, leading to system failure.

We propose a fundamentally different approach: allow richer error models that allow the probability of a failure to be explicitly modeled. In other words, we optimize the map while
simultaneously determining which loop closures are correct from within a single integrated Bayesian framework. Unlike earlier multiple-hypothesis approaches, our approach avoids
exponential memory complexity and is fast enough for realtime performance.

We show that the proposed method not only allows loop closing errors to be automatically identified, but also that in extreme cases, the “front-end” loop-validation systems can be unnecessary. We demonstrate our system both on standard benchmarks and on the real-world datasets that motivated this work.

Link:
Robotics: Science and Systems(RSS), 2012
LocalLink
http://april.eecs.umich.edu/papers/details.php?name=olson2012rss
http://www.roboticsproceedings.org/rss08/p40.pdf

Lab meeting Dec. 12, 2012 (Alan): A Simple Prior-free Method for Non-Rigid Structure-from-Motion Factorization (CVPR 2012 Best Paper Award)

Title: A Simple Prior-free Method for Non-Rigid Structure-from-Motion Factorization (CVPR 2012 Best Paper Award)
Authors: Yuchao Dai, Hongdong Li, Mingyi He

Abstract:
This paper proposes a simple “prior-free” method for solving non-rigid structure-from-motion factorization problems. Other than using the basic low-rank condition, our method does not assume any extra prior knowledge about the nonrigid scene or about the camera motions. Yet, it runs reliably, produces optimal result, and does not suffer from the inherent basis-ambiguity issue which plagued many conventional nonrigid factorization techniques.

Our method is easy to implement, which involves solving no more than an SDP (semi-definite programming) of small and fixed size, a linear Least-Squares or trace-norm minimization. Extensive experiments have demonstrated that it outperforms most of the existing linear methods of nonrigid factorization. This paper offers not only new theoretical insight, but also a practical, everyday solution, to non-rigid structure-from-motion.

Link

Lab meeting Dec 5th 2012 (Jim): Imitation Learning by Coaching

Title: Imitation Learning by Coaching
Authors: He He, Hal Daumé III and Jason Eisner
Neural Information Processing Systems (NIPS), 2012

Abstract:
... we propose to use a coach that demonstrates easy-to-learn actions for the learner and gradually approaches the oracle. ... We apply our algorithm to cost-sensitive dynamic feature selection, a hard decision problem that considers a user-specified accuracy-cost trade-off. ...

Link

NTU CSIE Talk: [2012-11-09] Dr. Koji Yatani, "A Ph.D. – What does it take?"

Title: A Ph.D. – What does it take?

Date: 2012-11-09 2:20pm

Location: R103

Speaker: Dr. Koji Yatani, Microsoft Research Asia

 

Abstract:

 

Getting a Ph.D. surely needs long effort, but why? Of course, research takes time, but a Ph.D. is not just about research. A Ph.D. student needs to be more than just a research person to be a successful Ph.D. This talk is not about a collection of my research projects (although I will introduce some of them a bit); rather, a collection of my experience in research at University of Toronto, Microsoft Research Asia, and industry labs where I did my internship. Through this talk, I will attempt to share my thoughts on what I believe a Ph.D. student should do and learn before getting her Ph.D. Your honest discussions, opinions and feedback would be greatly appreciated.

 

Biography: 

 

Dr. Koji Yatani (http://yatani.jp) is an associate researcher in Human-Computer Interaction Group at Microsoft Research Asia. His main research interests lie in Human-Computer Interaction (HCI) and its intersections with Ubiquitous Computing and Computational Linguistics. More specifically, he is interested in designing new forms of interacting with mobile devices, and developing new hardware and sensing technologies to support user interactions in mobile/ubiquitous computing environments. He is also interested in developing interactive systems and exploring new applications using computational linguistics methods.

 

He received B.Eng. and M.Sci. from University of Tokyo in 2003 and 2005, respectively, and his Ph.D. in Computer Science from University of Toronto in 2011. On November 2011, he joined HCI group at Microsoft Research Asia in Beijing. He was a recipient of NTT Docomo Scholarship (October 2003 -- March 2005), and Japan Society for the Promotion of Science Research Fellowship for Young Scientists (April 2005 -- March 2006). He received the Best Paper Award at CHI 2011. He served as a program committee on CHI 2013, Ubicomp 2012, and WHC 2013. He also served as a Mentoring co-chair on ITS 2012.

Lab meeting Oct 17th 2012 (Hank): Motion Segmentation of Multiple Objects from a Freely Moving Monocular Camera

Lab meeting Oct 17th 2012 (Hank): Motion Segmentation of Multiple Objects from a Freely Moving Monocular Camera

Link

Presented by Hank Lin

From ICRA2012

Authors: Rahul Kumar Namdev, Abhijit Kundu, K Madhava Krishna and C. V. Jawahar


Abstract:
Motion segmentation or segmentation of moving
objects is an inevitable component for mobile robotic systems
such as the case with robots performing SLAM and collision
avoidance in dynamic worlds. This paper proposes an incre-mental motion segmentation system that efficiently segments
multiple moving objects and simultaneously build the map of
the environment using visual SLAM modules. Multiple cues
based on optical flow and two view geometry are integrated
to achieve this segmentation. A dense optical flow algorithm
provides for dense tracking of features. Motion potentials based
on geometry are computed for each of these dense tracks. These
geometric potentials along with optical flow potentials are used
to form a graph like structure. A graph based segmentation
algorithm then clusters together nodes of similar potentials
to form the eventual motion segments. Experimental results
of high quality segmentation on different publicly available
datasets demonstrate the effectiveness of our method.

Lab meeting Oct 3rd 2012 (Gene): Decentralised Cooperative Localisation for Heterogeneous Teams of Mobile Robots

Link

Presented by Chun-Kai (Gene) Chang

From ICRA2011 Australian Centre for Field Robotics, University of Sydney, NSW, Australia

Authors: Tim Bailey, Mitch Bryson, Hua Mu , John Vial, Lachlan McCalman and Hugh Durrant-Whyte


Abstract:
This paper presents a distributed algorithm for
performing joint localisation of a team of robots. The mobile
robots have heterogeneous sensing capabilities, with some having
high quality inertial and exteroceptive sensing, while others have
only low quality sensing or none at all. By sharing information,
a combined estimate of all robot poses is obtained. Interrobot
range-bearing measurements provide the mechanism for
transferring pose information from well-localised vehicles to those
less capable.
In our proposed formulation, high frequency egocentric data
(e.g., odometry, IMU, GPS) is fused locally on each platform. This
is the distributed part of the algorithm. Inter-robot measurements,
and accompanying state estimates, are communicated to a central
server, which generates an optimal minimum mean-squared
estimate of all robot poses. This server is easily duplicated for
full redundant decentralisation. Communication and computation
are efficient due to the sparseness properties of the informationform
Gaussian representation. A team of three indoor mobile
robots equipped with lasers, odometry and inertial sensing provides
experimental verification of the algorithms effectiveness in
combining location information.

Lab meeting Sep 12th 2012 (Chih-Chung): Global motion planning under uncertain motion, sensing,and environment map

[LINK]

Presented by Chih-Chung

From Autonomous Robots, Volume 33, No.3, 2012, pp. 255-272

Authors:
Hanna Kurniawati · Tirthankar Bandyopadhyay ·
Nicholas M. Patrikalakis

Abstract:
Uncertainty in motion planning is often caused by
three main sources: motion error, sensing error, and imperfect
environment map. Despite the significant effect of all
three sources of uncertainty to motion planning problems,
most planners take into account only one or at most two
of them. We propose a new motion planner, called Guided
Cluster Sampling (GCS), that takes into account all three
sources of uncertainty for robots with active sensing capabilities.
GCS uses the Partially Observable Markov Decision
Process (POMDP) framework and the point-based
POMDP approach. Although point-based POMDPs have
shown impressive progress over the past few years, it performs
poorly when the environment map is imperfect. This
poor performance is due to the extremely high dimensional
state space, which translates to the extremely large belief
space B.
We alleviate this problem by constructing a more suitable
sampling distribution based on the observations that when the
robot has active sensing capability, B can be partitioned
into a collection of much smaller sub-spaces, and an optimal
policy can often be generated by sufficient sampling of
a small subset of the collection. Utilizing these observations,
GCS samples B in two-stages, a subspace is sampled from
the collection and then a belief is sampled from the subspace.

It uses information from the set of sampled sub-spaces
and sampled beliefs to guide subsequent sampling. Simulation
results on marine robotics scenarios suggest that GCS
can generate reasonable policies for motion planning problems
with uncertain motion, sensing, and environment map,
that are unsolvable by the best point-based POMDPs today.
Furthermore, GCS handles POMDPs with continuous state,
action, and observation spaces. We show that for a class of
POMDPs that often occur in robot motion planning, given
enough time, GCS converges to the optimal policy.

To the best of our knowledge, this is the first convergence
result for point-based POMDPs with continuous action
space.

Lab Meeting June 6th, 2012 (Chiao-Hui ): Robot Musical Accompaniment: Integrating Audio and Visual Cues for Real-time Synchronization with a Human Flutist

Chiao-Hui will present the following paper:
Robot Musical Accompaniment: Integrating Audio and Visual Cues for Real-time Synchronization with a Human Flutist

Authors: Angelica Lim, Takeshi Mizumoto, Louis-Kenzo Cahier, Takuma Otsuka, Toru Takahashi, Kazunori Komatani, Tetsuya Ogata and Hiroshi G. Okuno

From: IROS 2010

Abstract:
Musicians often have the following problem: they have a music score that requires 2 or more players, but they have no one with whom to practice. So far, score-playing music robots exist, but they lack adaptive abilities to synchronize with fellow players’ tempo variations. In other words, if the human speeds up their play, the robot should also increase its speed. However, computer accompaniment systems allow exactly this kind of adaptive ability. We present a first step towards giving these accompaniment abilities to a music robot. We introduce a new paradigm of beat tracking using 2 types of sensory input – visual and audio – using our own visual cue recognition system and state-of-the-art acoustic onset detection techniques. Preliminary experiments suggest that by coupling these two modalities, a robot accompanist can start and stop a performance in synchrony with a flutist, and detect tempo changes within half a second.

Link

Lab Meeting May 30th, 2012 (Wei-Shin): Progress Report

I will present my current progress on chair manipulation at lab meeting.  (Borrowed account for posting.)

Lab Meeting May 22th, 2012 (Mark):Strong supervision from weak annotation: Interactive training of deformable part models

We propose a framework for large scale learning and annotation of structured models. The system interleaves interactive labeling (where the current model is used to semi-automate the labeling of a new example) and online learning (where a newly labeled example is used to update the current model parameters). This framework is scalable to large datasets and complex image models and is shown to have excellent theoretical and practical properties in terms of train time, optimality guarantees, and bounds on the amount of annotation effort per image. We apply this framework to part-based detection, and introduce a novel algorithm for interactive labeling of deformable part models. The labeling tool updates and displays in real-time the maximum likelihood location of all parts as the user clicks and drags the location of one or more parts. We demonstrate that the system can be used to efficiently and robustly train part and pose detectors on the CUB Birds-200-a challenging dataset of birds in unconstrained pose and environment.
paper link

[Robot Perception and Learning] Meeting 2012/05/02 (Andi):Energy Based Multiple Model Fitting for Non-Rigid Structure from Motion

Energy Based Multiple Model Fitting for Non-Rigid Structure from Motion

Authors: Chris Russell, Joao Fayad, Lourdes Agapito

From: CVPR '11

Abstract: 

In this paper we reformulate the 3D reconstruction of deformable surfaces from monocular video sequences as a labeling problem. We solve simultaneously for the assignment of feature points to multiple local deformation models and the fitting of models to points to minimize a geometric cost, subject to a spatial constraint that neighboring points should also belong to the same model.

Piecewise reconstruction methods rely on features shared between models to enforce global consistency on the 3D surface. To account for this overlap between regions, we consider a super-set of the classic labeling problem in which a set of labels, instead of a single one, is assigned to each variable. We propose a mathematical formulation of this new model and show how it can be efficiently optimized with a variant of -expansion. We demonstrate how this framework can be applied to Non-Rigid Structure from Motion and leads to simpler explanations of the same data. Compared to existing methods run on the same data, our approach has up to half the reconstruction error, and is more robust to over-fitting and outliers.

Link: get paper here

Lab Meeting April 25th, 2012 (David): Progress Report

    I will present my research progress on laser scanner and stereo camera sensor fusion, with application on pedestrian detection.

Lab Meeting April 11th, 2012 (Jeff): Progress Report

The progress of my current work on Failure Tolerance SLAM with RFID tags would be shared during the meeting.