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2018


Deep Inertial Poser: Learning to Reconstruct Human Pose from Sparse Inertial Measurements in Real Time
Deep Inertial Poser: Learning to Reconstruct Human Pose from Sparse Inertial Measurements in Real Time

Huang, Y., Kaufmann, M., Aksan, E., Black, M. J., Hilliges, O., Pons-Moll, G.

ACM Transactions on Graphics, (Proc. SIGGRAPH Asia), 37, pages: 185:1-185:15, ACM, November 2018, Two first authors contributed equally (article)

Abstract
We demonstrate a novel deep neural network capable of reconstructing human full body pose in real-time from 6 Inertial Measurement Units (IMUs) worn on the user's body. In doing so, we address several difficult challenges. First, the problem is severely under-constrained as multiple pose parameters produce the same IMU orientations. Second, capturing IMU data in conjunction with ground-truth poses is expensive and difficult to do in many target application scenarios (e.g., outdoors). Third, modeling temporal dependencies through non-linear optimization has proven effective in prior work but makes real-time prediction infeasible. To address this important limitation, we learn the temporal pose priors using deep learning. To learn from sufficient data, we synthesize IMU data from motion capture datasets. A bi-directional RNN architecture leverages past and future information that is available at training time. At test time, we deploy the network in a sliding window fashion, retaining real time capabilities. To evaluate our method, we recorded DIP-IMU, a dataset consisting of 10 subjects wearing 17 IMUs for validation in 64 sequences with 330,000 time instants; this constitutes the largest IMU dataset publicly available. We quantitatively evaluate our approach on multiple datasets and show results from a real-time implementation. DIP-IMU and the code are available for research purposes.

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data code pdf preprint errata video DOI Project Page [BibTex]

2018


data code pdf preprint errata video DOI Project Page [BibTex]


A Value-Driven Eldercare Robot: Virtual and Physical Instantiations of a Case-Supported Principle-Based Behavior Paradigm
A Value-Driven Eldercare Robot: Virtual and Physical Instantiations of a Case-Supported Principle-Based Behavior Paradigm

Anderson, M., Anderson, S., Berenz, V.

Proceedings of the IEEE, pages: 1,15, October 2018 (article)

Abstract
In this paper, a case-supported principle-based behavior paradigm is proposed to help ensure ethical behavior of autonomous machines. We argue that ethically significant behavior of autonomous systems should be guided by explicit ethical principles determined through a consensus of ethicists. Such a consensus is likely to emerge in many areas in which autonomous systems are apt to be deployed and for the actions they are liable to undertake. We believe that this is the case since we are more likely to agree on how machines ought to treat us than on how human beings ought to treat one another. Given such a consensus, particular cases of ethical dilemmas where ethicists agree on the ethically relevant features and the right course of action can be used to help discover principles that balance these features when they are in conflict. Such principles not only help ensure ethical behavior of complex and dynamic systems but also can serve as a basis for justification of this behavior. The requirements, methods, implementation, and evaluation components of the paradigm are detailed as well as its instantiation in both a simulated and real robot functioning in the domain of eldercare.

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link (url) DOI [BibTex]


Deep Neural Network-based Cooperative Visual Tracking through Multiple Micro Aerial Vehicles
Deep Neural Network-based Cooperative Visual Tracking through Multiple Micro Aerial Vehicles

Price, E., Lawless, G., Ludwig, R., Martinovic, I., Buelthoff, H. H., Black, M. J., Ahmad, A.

IEEE Robotics and Automation Letters, Robotics and Automation Letters, 3(4):3193-3200, IEEE, October 2018, Also accepted and presented in the 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). (article)

Abstract
Multi-camera tracking of humans and animals in outdoor environments is a relevant and challenging problem. Our approach to it involves a team of cooperating micro aerial vehicles (MAVs) with on-board cameras only. DNNs often fail at objects with small scale or far away from the camera, which are typical characteristics of a scenario with aerial robots. Thus, the core problem addressed in this paper is how to achieve on-board, online, continuous and accurate vision-based detections using DNNs for visual person tracking through MAVs. Our solution leverages cooperation among multiple MAVs and active selection of most informative regions of image. We demonstrate the efficiency of our approach through simulations with up to 16 robots and real robot experiments involving two aerial robots tracking a person, while maintaining an active perception-driven formation. ROS-based source code is provided for the benefit of the community.

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Published Version link (url) DOI [BibTex]

Published Version link (url) DOI [BibTex]


First Impressions of Personality Traits From Body Shapes
First Impressions of Personality Traits From Body Shapes

Hu, Y., Parde, C. J., Hill, M. Q., Mahmood, N., O’Toole, A. J.

Psychological Science, 29(12):1969-–1983, October 2018 (article)

Abstract
People infer the personalities of others from their facial appearance. Whether they do so from body shapes is less studied. We explored personality inferences made from body shapes. Participants rated personality traits for male and female bodies generated with a three-dimensional body model. Multivariate spaces created from these ratings indicated that people evaluate bodies on valence and agency in ways that directly contrast positive and negative traits from the Big Five domains. Body-trait stereotypes based on the trait ratings revealed a myriad of diverse body shapes that typify individual traits. Personality-trait profiles were predicted reliably from a subset of the body-shape features used to specify the three-dimensional bodies. Body features related to extraversion and conscientiousness were predicted with the highest consensus, followed by openness traits. This study provides the first comprehensive look at the range, diversity, and reliability of personality inferences that people make from body shapes.

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publisher site pdf DOI [BibTex]

publisher site pdf DOI [BibTex]


Playful: Reactive Programming for Orchestrating Robotic Behavior
Playful: Reactive Programming for Orchestrating Robotic Behavior

Berenz, V., Schaal, S.

IEEE Robotics Automation Magazine, 25(3):49-60, September 2018 (article) In press

Abstract
For many service robots, reactivity to changes in their surroundings is a must. However, developing software suitable for dynamic environments is difficult. Existing robotic middleware allows engineers to design behavior graphs by organizing communication between components. But because these graphs are structurally inflexible, they hardly support the development of complex reactive behavior. To address this limitation, we propose Playful, a software platform that applies reactive programming to the specification of robotic behavior.

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playful website playful_IEEE_RAM link (url) DOI [BibTex]


ClusterNet: Instance Segmentation in RGB-D Images
ClusterNet: Instance Segmentation in RGB-D Images

Shao, L., Tian, Y., Bohg, J.

arXiv, September 2018, Submitted to ICRA'19 (article) Submitted

Abstract
We propose a method for instance-level segmentation that uses RGB-D data as input and provides detailed information about the location, geometry and number of {\em individual\/} objects in the scene. This level of understanding is fundamental for autonomous robots. It enables safe and robust decision-making under the large uncertainty of the real-world. In our model, we propose to use the first and second order moments of the object occupancy function to represent an object instance. We train an hourglass Deep Neural Network (DNN) where each pixel in the output votes for the 3D position of the corresponding object center and for the object's size and pose. The final instance segmentation is achieved through clustering in the space of moments. The object-centric training loss is defined on the output of the clustering. Our method outperforms the state-of-the-art instance segmentation method on our synthesized dataset. We show that our method generalizes well on real-world data achieving visually better segmentation results.

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link (url) [BibTex]

link (url) [BibTex]


Visual Perception and Evaluation of Photo-Realistic Self-Avatars From {3D} Body Scans in Males and Females
Visual Perception and Evaluation of Photo-Realistic Self-Avatars From 3D Body Scans in Males and Females

Thaler, A., Piryankova, I., Stefanucci, J. K., Pujades, S., de la Rosa, S., Streuber, S., Romero, J., Black, M. J., Mohler, B. J.

Frontiers in ICT, 5, pages: 1-14, September 2018 (article)

Abstract
The creation or streaming of photo-realistic self-avatars is important for virtual reality applications that aim for perception and action to replicate real world experience. The appearance and recognition of a digital self-avatar may be especially important for applications related to telepresence, embodied virtual reality, or immersive games. We investigated gender differences in the use of visual cues (shape, texture) of a self-avatar for estimating body weight and evaluating avatar appearance. A full-body scanner was used to capture each participant's body geometry and color information and a set of 3D virtual avatars with realistic weight variations was created based on a statistical body model. Additionally, a second set of avatars was created with an average underlying body shape matched to each participant’s height and weight. In four sets of psychophysical experiments, the influence of visual cues on the accuracy of body weight estimation and the sensitivity to weight changes was assessed by manipulating body shape (own, average) and texture (own photo-realistic, checkerboard). The avatars were presented on a large-screen display, and participants responded to whether the avatar's weight corresponded to their own weight. Participants also adjusted the avatar's weight to their desired weight and evaluated the avatar's appearance with regard to similarity to their own body, uncanniness, and their willingness to accept it as a digital representation of the self. The results of the psychophysical experiments revealed no gender difference in the accuracy of estimating body weight in avatars. However, males accepted a larger weight range of the avatars as corresponding to their own. In terms of the ideal body weight, females but not males desired a thinner body. With regard to the evaluation of avatar appearance, the questionnaire responses suggest that own photo-realistic texture was more important to males for higher similarity ratings, while own body shape seemed to be more important to females. These results argue for gender-specific considerations when creating self-avatars.

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pdf DOI [BibTex]

pdf DOI [BibTex]


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Learning an Approximate Model Predictive Controller with Guarantees

Hertneck, M., Koehler, J., Trimpe, S., Allgöwer, F.

IEEE Control Systems Letters, 2(3):543-548, July 2018 (article)

Abstract
A supervised learning framework is proposed to approximate a model predictive controller (MPC) with reduced computational complexity and guarantees on stability and constraint satisfaction. The framework can be used for a wide class of nonlinear systems. Any standard supervised learning technique (e.g. neural networks) can be employed to approximate the MPC from samples. In order to obtain closed-loop guarantees for the learned MPC, a robust MPC design is combined with statistical learning bounds. The MPC design ensures robustness to inaccurate inputs within given bounds, and Hoeffding’s Inequality is used to validate that the learned MPC satisfies these bounds with high confidence. The result is a closed-loop statistical guarantee on stability and constraint satisfaction for the learned MPC. The proposed learning-based MPC framework is illustrated on a nonlinear benchmark problem, for which we learn a neural network controller with guarantees.

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arXiv PDF DOI [BibTex]

arXiv PDF DOI [BibTex]


Robust Physics-based Motion Retargeting with Realistic Body Shapes
Robust Physics-based Motion Retargeting with Realistic Body Shapes

Borno, M. A., Righetti, L., Black, M. J., Delp, S. L., Fiume, E., Romero, J.

Computer Graphics Forum, 37, pages: 6:1-12, July 2018 (article)

Abstract
Motion capture is often retargeted to new, and sometimes drastically different, characters. When the characters take on realistic human shapes, however, we become more sensitive to the motion looking right. This means adapting it to be consistent with the physical constraints imposed by different body shapes. We show how to take realistic 3D human shapes, approximate them using a simplified representation, and animate them so that they move realistically using physically-based retargeting. We develop a novel spacetime optimization approach that learns and robustly adapts physical controllers to new bodies and constraints. The approach automatically adapts the motion of the mocap subject to the body shape of a target subject. This motion respects the physical properties of the new body and every body shape results in a different and appropriate movement. This makes it easy to create a varied set of motions from a single mocap sequence by simply varying the characters. In an interactive environment, successful retargeting requires adapting the motion to unexpected external forces. We achieve robustness to such forces using a novel LQR-tree formulation. We show that the simulated motions look appropriate to each character’s anatomy and their actions are robust to perturbations.

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pdf video Project Page Project Page [BibTex]

pdf video Project Page Project Page [BibTex]


Real-time Perception meets Reactive Motion Generation
Real-time Perception meets Reactive Motion Generation

(Best Systems Paper Finalists - Amazon Robotics Best Paper Awards in Manipulation)

Kappler, D., Meier, F., Issac, J., Mainprice, J., Garcia Cifuentes, C., Wüthrich, M., Berenz, V., Schaal, S., Ratliff, N., Bohg, J.

IEEE Robotics and Automation Letters, 3(3):1864-1871, July 2018 (article)

Abstract
We address the challenging problem of robotic grasping and manipulation in the presence of uncertainty. This uncertainty is due to noisy sensing, inaccurate models and hard-to-predict environment dynamics. Our approach emphasizes the importance of continuous, real-time perception and its tight integration with reactive motion generation methods. We present a fully integrated system where real-time object and robot tracking as well as ambient world modeling provides the necessary input to feedback controllers and continuous motion optimizers. Specifically, they provide attractive and repulsive potentials based on which the controllers and motion optimizer can online compute movement policies at different time intervals. We extensively evaluate the proposed system on a real robotic platform in four scenarios that exhibit either challenging workspace geometry or a dynamic environment. We compare the proposed integrated system with a more traditional sense-plan-act approach that is still widely used. In 333 experiments, we show the robustness and accuracy of the proposed system.

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arxiv video video link (url) DOI Project Page [BibTex]


Assessing body image in anorexia nervosa using biometric self-avatars in virtual reality: Attitudinal components rather than visual body size estimation are distorted
Assessing body image in anorexia nervosa using biometric self-avatars in virtual reality: Attitudinal components rather than visual body size estimation are distorted

Mölbert, S. C., Thaler, A., Mohler, B. J., Streuber, S., Romero, J., Black, M. J., Zipfel, S., Karnath, H., Giel, K. E.

Psychological Medicine, 48(4):642-653, March 2018 (article)

Abstract
Background: Body image disturbance (BID) is a core symptom of anorexia nervosa (AN), but as yet distinctive features of BID are unknown. The present study aimed at disentangling perceptual and attitudinal components of BID in AN. Methods: We investigated n=24 women with AN and n=24 controls. Based on a 3D body scan, we created realistic virtual 3D bodies (avatars) for each participant that were varied through a range of ±20% of the participants' weights. Avatars were presented in a virtual reality mirror scenario. Using different psychophysical tasks, participants identified and adjusted their actual and their desired body weight. To test for general perceptual biases in estimating body weight, a second experiment investigated perception of weight and shape matched avatars with another identity. Results: Women with AN and controls underestimated their weight, with a trend that women with AN underestimated more. The average desired body of controls had normal weight while the average desired weight of women with AN corresponded to extreme AN (DSM-5). Correlation analyses revealed that desired body weight, but not accuracy of weight estimation, was associated with eating disorder symptoms. In the second experiment, both groups estimated accurately while the most attractive body was similar to Experiment 1. Conclusions: Our results contradict the widespread assumption that patients with AN overestimate their body weight due to visual distortions. Rather, they illustrate that BID might be driven by distorted attitudes with regard to the desired body. Clinical interventions should aim at helping patients with AN to change their desired weight.

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doi pdf DOI Project Page [BibTex]


Body size estimation of self and others in females varying in {BMI}
Body size estimation of self and others in females varying in BMI

Thaler, A., Geuss, M. N., Mölbert, S. C., Giel, K. E., Streuber, S., Romero, J., Black, M. J., Mohler, B. J.

PLoS ONE, 13(2), Febuary 2018 (article)

Abstract
Previous literature suggests that a disturbed ability to accurately identify own body size may contribute to overweight. Here, we investigated the influence of personal body size, indexed by body mass index (BMI), on body size estimation in a non-clinical population of females varying in BMI. We attempted to disentangle general biases in body size estimates and attitudinal influences by manipulating whether participants believed the body stimuli (personalized avatars with realistic weight variations) represented their own body or that of another person. Our results show that the accuracy of own body size estimation is predicted by personal BMI, such that participants with lower BMI underestimated their body size and participants with higher BMI overestimated their body size. Further, participants with higher BMI were less likely to notice the same percentage of weight gain than participants with lower BMI. Importantly, these results were only apparent when participants were judging a virtual body that was their own identity (Experiment 1), but not when they estimated the size of a body with another identity and the same underlying body shape (Experiment 2a). The different influences of BMI on accuracy of body size estimation and sensitivity to weight change for self and other identity suggests that effects of BMI on visual body size estimation are self-specific and not generalizable to other bodies.

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pdf DOI Project Page [BibTex]


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Distributed Event-Based State Estimation for Networked Systems: An LMI Approach

Muehlebach, M., Trimpe, S.

IEEE Transactions on Automatic Control, 63(1):269-276, January 2018 (article)

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arXiv (extended version) DOI Project Page [BibTex]

arXiv (extended version) DOI Project Page [BibTex]


Combining learned and analytical models for predicting action effects
Combining learned and analytical models for predicting action effects

Kloss, A., Schaal, S., Bohg, J.

arXiv, 2018 (article) Submitted

Abstract
One of the most basic skills a robot should possess is predicting the effect of physical interactions with objects in the environment. This enables optimal action selection to reach a certain goal state. Traditionally, dynamics are approximated by physics-based analytical models. These models rely on specific state representations that may be hard to obtain from raw sensory data, especially if no knowledge of the object shape is assumed. More recently, we have seen learning approaches that can predict the effect of complex physical interactions directly from sensory input. It is however an open question how far these models generalize beyond their training data. In this work, we investigate the advantages and limitations of neural network based learning approaches for predicting the effects of actions based on sensory input and show how analytical and learned models can be combined to leverage the best of both worlds. As physical interaction task, we use planar pushing, for which there exists a well-known analytical model and a large real-world dataset. We propose to use a convolutional neural network to convert raw depth images or organized point clouds into a suitable representation for the analytical model and compare this approach to using neural networks for both, perception and prediction. A systematic evaluation of the proposed approach on a very large real-world dataset shows two main advantages of the hybrid architecture. Compared to a pure neural network, it significantly (i) reduces required training data and (ii) improves generalization to novel physical interaction.

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arXiv pdf link (url) [BibTex]


Temporal Human Action Segmentation via Dynamic Clustering
Temporal Human Action Segmentation via Dynamic Clustering

Zhang, Y., Sun, H., Tang, S., Neumann, H.

arXiv preprint arXiv:1803.05790, 2018 (article)

Abstract
We present an effective dynamic clustering algorithm for the task of temporal human action segmentation, which has comprehensive applications such as robotics, motion analysis, and patient monitoring. Our proposed algorithm is unsupervised, fast, generic to process various types of features, and applica- ble in both the online and offline settings. We perform extensive experiments of processing data streams, and show that our algorithm achieves the state-of- the-art results for both online and offline settings.

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link (url) [BibTex]

link (url) [BibTex]


Motion Segmentation & Multiple Object Tracking by Correlation Co-Clustering
Motion Segmentation & Multiple Object Tracking by Correlation Co-Clustering

Keuper, M., Tang, S., Andres, B., Brox, T., Schiele, B.

IEEE Transactions on Pattern Analysis and Machine Intelligence, 2018 (article)

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pdf DOI Project Page [BibTex]

pdf DOI Project Page [BibTex]

2016


A New Perspective and Extension of the Gaussian Filter
A New Perspective and Extension of the Gaussian Filter

Wüthrich, M., Trimpe, S., Garcia Cifuentes, C., Kappler, D., Schaal, S.

The International Journal of Robotics Research, 35(14):1731-1749, December 2016 (article)

Abstract
The Gaussian Filter (GF) is one of the most widely used filtering algorithms; instances are the Extended Kalman Filter, the Unscented Kalman Filter and the Divided Difference Filter. The GF represents the belief of the current state by a Gaussian distribution, whose mean is an affine function of the measurement. We show that this representation can be too restrictive to accurately capture the dependences in systems with nonlinear observation models, and we investigate how the GF can be generalized to alleviate this problem. To this end, we view the GF as the solution to a constrained optimization problem. From this new perspective, the GF is seen as a special case of a much broader class of filters, obtained by relaxing the constraint on the form of the approximate posterior. On this basis, we outline some conditions which potential generalizations have to satisfy in order to maintain the computational efficiency of the GF. We propose one concrete generalization which corresponds to the standard GF using a pseudo measurement instead of the actual measurement. Extending an existing GF implementation in this manner is trivial. Nevertheless, we show that this small change can have a major impact on the estimation accuracy.

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PDF DOI Project Page [BibTex]

2016


PDF DOI Project Page [BibTex]


Creating body shapes from verbal descriptions by linking similarity spaces
Creating body shapes from verbal descriptions by linking similarity spaces

Hill, M. Q., Streuber, S., Hahn, C. A., Black, M. J., O’Toole, A. J.

Psychological Science, 27(11):1486-1497, November 2016, (article)

Abstract
Brief verbal descriptions of bodies (e.g. curvy, long-legged) can elicit vivid mental images. The ease with which we create these mental images belies the complexity of three-dimensional body shapes. We explored the relationship between body shapes and body descriptions and show that a small number of words can be used to generate categorically accurate representations of three-dimensional bodies. The dimensions of body shape variation that emerged in a language-based similarity space were related to major dimensions of variation computed directly from three-dimensional laser scans of 2094 bodies. This allowed us to generate three-dimensional models of people in the shape space using only their coordinates on analogous dimensions in the language-based description space. Human descriptions of photographed bodies and their corresponding models matched closely. The natural mapping between the spaces illustrates the role of language as a concise code for body shape, capturing perceptually salient global and local body features.

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pdf [BibTex]

pdf [BibTex]


{Body Talk}: Crowdshaping Realistic {3D} Avatars with Words
Body Talk: Crowdshaping Realistic 3D Avatars with Words

Streuber, S., Quiros-Ramirez, M. A., Hill, M. Q., Hahn, C. A., Zuffi, S., O’Toole, A., Black, M. J.

ACM Trans. Graph. (Proc. SIGGRAPH), 35(4):54:1-54:14, July 2016 (article)

Abstract
Realistic, metrically accurate, 3D human avatars are useful for games, shopping, virtual reality, and health applications. Such avatars are not in wide use because solutions for creating them from high-end scanners, low-cost range cameras, and tailoring measurements all have limitations. Here we propose a simple solution and show that it is surprisingly accurate. We use crowdsourcing to generate attribute ratings of 3D body shapes corresponding to standard linguistic descriptions of 3D shape. We then learn a linear function relating these ratings to 3D human shape parameters. Given an image of a new body, we again turn to the crowd for ratings of the body shape. The collection of linguistic ratings of a photograph provides remarkably strong constraints on the metric 3D shape. We call the process crowdshaping and show that our Body Talk system produces shapes that are perceptually indistinguishable from bodies created from high-resolution scans and that the metric accuracy is sufficient for many tasks. This makes body “scanning” practical without a scanner, opening up new applications including database search, visualization, and extracting avatars from books.

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pdf web tool video talk (ppt) [BibTex]

pdf web tool video talk (ppt) [BibTex]


Capturing Hands in Action using Discriminative Salient Points and Physics Simulation
Capturing Hands in Action using Discriminative Salient Points and Physics Simulation

Tzionas, D., Ballan, L., Srikantha, A., Aponte, P., Pollefeys, M., Gall, J.

International Journal of Computer Vision (IJCV), 118(2):172-193, June 2016 (article)

Abstract
Hand motion capture is a popular research field, recently gaining more attention due to the ubiquity of RGB-D sensors. However, even most recent approaches focus on the case of a single isolated hand. In this work, we focus on hands that interact with other hands or objects and present a framework that successfully captures motion in such interaction scenarios for both rigid and articulated objects. Our framework combines a generative model with discriminatively trained salient points to achieve a low tracking error and with collision detection and physics simulation to achieve physically plausible estimates even in case of occlusions and missing visual data. Since all components are unified in a single objective function which is almost everywhere differentiable, it can be optimized with standard optimization techniques. Our approach works for monocular RGB-D sequences as well as setups with multiple synchronized RGB cameras. For a qualitative and quantitative evaluation, we captured 29 sequences with a large variety of interactions and up to 150 degrees of freedom.

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Website pdf link (url) DOI Project Page [BibTex]

Website pdf link (url) DOI Project Page [BibTex]


Human Pose Estimation from Video and IMUs
Human Pose Estimation from Video and IMUs

Marcard, T. V., Pons-Moll, G., Rosenhahn, B.

Transactions on Pattern Analysis and Machine Intelligence PAMI, 38(8):1533-1547, January 2016 (article)

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data pdf dataset_documentation [BibTex]

data pdf dataset_documentation [BibTex]


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Supplemental material for ’Communication Rate Analysis for Event-based State Estimation’

Ebner, S., Trimpe, S.

Max Planck Institute for Intelligent Systems, January 2016 (techreport)

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PDF [BibTex]

PDF [BibTex]


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Probabilistic Inference for Determining Options in Reinforcement Learning

Daniel, C., van Hoof, H., Peters, J., Neumann, G.

Machine Learning, Special Issue, 104(2):337-357, (Editors: Gärtner, T., Nanni, M., Passerini, A. and Robardet, C.), European Conference on Machine Learning im Machine Learning, Journal Track, 2016, Best Student Paper Award of ECML-PKDD 2016 (article)

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DOI Project Page [BibTex]

DOI Project Page [BibTex]


Shape estimation of subcutaneous adipose tissue using an articulated statistical shape model
Shape estimation of subcutaneous adipose tissue using an articulated statistical shape model

Yeo, S. Y., Romero, J., Loper, M., Machann, J., Black, M.

Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 0(0):1-8, 2016 (article)

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publisher website preprint pdf link (url) DOI Project Page [BibTex]

publisher website preprint pdf link (url) DOI Project Page [BibTex]


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Event-based Sampling for Reducing Communication Load in Realtime Human Motion Analysis by Wireless Inertial Sensor Networks

Laidig, D., Trimpe, S., Seel, T.

Current Directions in Biomedical Engineering, 2(1):711-714, De Gruyter, 2016 (article)

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PDF DOI [BibTex]

PDF DOI [BibTex]


The GRASP Taxonomy of Human Grasp Types
The GRASP Taxonomy of Human Grasp Types

Feix, T., Romero, J., Schmiedmayer, H., Dollar, A., Kragic, D.

Human-Machine Systems, IEEE Transactions on, 46(1):66-77, 2016 (article)

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publisher website pdf DOI Project Page [BibTex]

publisher website pdf DOI Project Page [BibTex]


Map-Based Probabilistic Visual Self-Localization
Map-Based Probabilistic Visual Self-Localization

Brubaker, M. A., Geiger, A., Urtasun, R.

IEEE Trans. on Pattern Analysis and Machine Intelligence (PAMI), 2016 (article)

Abstract
Accurate and efficient self-localization is a critical problem for autonomous systems. This paper describes an affordable solution to vehicle self-localization which uses odometry computed from two video cameras and road maps as the sole inputs. The core of the method is a probabilistic model for which an efficient approximate inference algorithm is derived. The inference algorithm is able to utilize distributed computation in order to meet the real-time requirements of autonomous systems in some instances. Because of the probabilistic nature of the model the method is capable of coping with various sources of uncertainty including noise in the visual odometry and inherent ambiguities in the map (e.g., in a Manhattan world). By exploiting freely available, community developed maps and visual odometry measurements, the proposed method is able to localize a vehicle to 4m on average after 52 seconds of driving on maps which contain more than 2,150km of drivable roads.

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pdf Project Page [BibTex]

pdf Project Page [BibTex]


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Momentum Control with Hierarchical Inverse Dynamics on a Torque-Controlled Humanoid

Herzog, A., Rotella, N., Mason, S., Grimminger, F., Schaal, S., Righetti, L.

Autonomous Robots, 40(3):473-491, 2016 (article)

Abstract
Hierarchical inverse dynamics based on cascades of quadratic programs have been proposed for the control of legged robots. They have important benefits but to the best of our knowledge have never been implemented on a torque controlled humanoid where model inaccuracies, sensor noise and real-time computation requirements can be problematic. Using a reformulation of existing algorithms, we propose a simplification of the problem that allows to achieve real-time control. Momentum-based control is integrated in the task hierarchy and a LQR design approach is used to compute the desired associated closed-loop behavior and improve performance. Extensive experiments on various balancing and tracking tasks show very robust performance in the face of unknown disturbances, even when the humanoid is standing on one foot. Our results demonstrate that hierarchical inverse dynamics together with momentum control can be efficiently used for feedback control under real robot conditions.

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link (url) DOI [BibTex]


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Bioinspired Motor Control for Articulated Robots [From the Guest Editors]

Vitiello, Nicola, Ijspeert, Auke J, Schaal, S.

IEEE Robotics {\&} Automation Magazine, 23(1):20-21, 2016 (article)

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[BibTex]

[BibTex]

2015


Scalable Robust Principal Component Analysis using {Grassmann} Averages
Scalable Robust Principal Component Analysis using Grassmann Averages

Hauberg, S., Feragen, A., Enficiaud, R., Black, M.

IEEE Trans. Pattern Analysis and Machine Intelligence (PAMI), December 2015 (article)

Abstract
In large datasets, manual data verification is impossible, and we must expect the number of outliers to increase with data size. While principal component analysis (PCA) can reduce data size, and scalable solutions exist, it is well-known that outliers can arbitrarily corrupt the results. Unfortunately, state-of-the-art approaches for robust PCA are not scalable. We note that in a zero-mean dataset, each observation spans a one-dimensional subspace, giving a point on the Grassmann manifold. We show that the average subspace corresponds to the leading principal component for Gaussian data. We provide a simple algorithm for computing this Grassmann Average (GA), and show that the subspace estimate is less sensitive to outliers than PCA for general distributions. Because averages can be efficiently computed, we immediately gain scalability. We exploit robust averaging to formulate the Robust Grassmann Average (RGA) as a form of robust PCA. The resulting Trimmed Grassmann Average (TGA) is appropriate for computer vision because it is robust to pixel outliers. The algorithm has linear computational complexity and minimal memory requirements. We demonstrate TGA for background modeling, video restoration, and shadow removal. We show scalability by performing robust PCA on the entire Star Wars IV movie; a task beyond any current method. Source code is available online.

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preprint pdf from publisher supplemental Project Page [BibTex]

2015


preprint pdf from publisher supplemental Project Page [BibTex]


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Distributed Event-based State Estimation

Trimpe, S.

Max Planck Institute for Intelligent Systems, November 2015 (techreport)

Abstract
An event-based state estimation approach for reducing communication in a networked control system is proposed. Multiple distributed sensor-actuator-agents observe a dynamic process and sporadically exchange their measurements and inputs over a bus network. Based on these data, each agent estimates the full state of the dynamic system, which may exhibit arbitrary inter-agent couplings. Local event-based protocols ensure that data is transmitted only when necessary to meet a desired estimation accuracy. This event-based scheme is shown to mimic a centralized Luenberger observer design up to guaranteed bounds, and stability is proven in the sense of bounded estimation errors for bounded disturbances. The stability result extends to the distributed control system that results when the local state estimates are used for distributed feedback control. Simulation results highlight the benefit of the event-based approach over classical periodic ones in reducing communication requirements.

am ics

arXiv [BibTex]

arXiv [BibTex]


{SMPL}: A Skinned Multi-Person Linear Model
SMPL: A Skinned Multi-Person Linear Model

Loper, M., Mahmood, N., Romero, J., Pons-Moll, G., Black, M. J.

ACM Trans. Graphics (Proc. SIGGRAPH Asia), 34(6):248:1-248:16, ACM, New York, NY, October 2015 (article)

Abstract
We present a learned model of human body shape and pose-dependent shape variation that is more accurate than previous models and is compatible with existing graphics pipelines. Our Skinned Multi-Person Linear model (SMPL) is a skinned vertex-based model that accurately represents a wide variety of body shapes in natural human poses. The parameters of the model are learned from data including the rest pose template, blend weights, pose-dependent blend shapes, identity-dependent blend shapes, and a regressor from vertices to joint locations. Unlike previous models, the pose-dependent blend shapes are a linear function of the elements of the pose rotation matrices. This simple formulation enables training the entire model from a relatively large number of aligned 3D meshes of different people in different poses. We quantitatively evaluate variants of SMPL using linear or dual-quaternion blend skinning and show that both are more accurate than a Blend-SCAPE model trained on the same data. We also extend SMPL to realistically model dynamic soft-tissue deformations. Because it is based on blend skinning, SMPL is compatible with existing rendering engines and we make it available for research purposes.

ps

pdf video code/model errata DOI Project Page Project Page [BibTex]

pdf video code/model errata DOI Project Page Project Page [BibTex]


Dyna: A Model of Dynamic Human Shape in Motion
Dyna: A Model of Dynamic Human Shape in Motion

Pons-Moll, G., Romero, J., Mahmood, N., Black, M. J.

ACM Transactions on Graphics, (Proc. SIGGRAPH), 34(4):120:1-120:14, ACM, August 2015 (article)

Abstract
To look human, digital full-body avatars need to have soft tissue deformations like those of real people. We learn a model of soft-tissue deformations from examples using a high-resolution 4D capture system and a method that accurately registers a template mesh to sequences of 3D scans. Using over 40,000 scans of ten subjects, we learn how soft tissue motion causes mesh triangles to deform relative to a base 3D body model. Our Dyna model uses a low-dimensional linear subspace to approximate soft-tissue deformation and relates the subspace coefficients to the changing pose of the body. Dyna uses a second-order auto-regressive model that predicts soft-tissue deformations based on previous deformations, the velocity and acceleration of the body, and the angular velocities and accelerations of the limbs. Dyna also models how deformations vary with a person’s body mass index (BMI), producing different deformations for people with different shapes. Dyna realistically represents the dynamics of soft tissue for previously unseen subjects and motions. We provide tools for animators to modify the deformations and apply them to new stylized characters.

ps

pdf preprint video data DOI Project Page Project Page [BibTex]

pdf preprint video data DOI Project Page Project Page [BibTex]


Linking Objects to Actions: Encoding of Target Object and Grasping Strategy in Primate Ventral Premotor Cortex
Linking Objects to Actions: Encoding of Target Object and Grasping Strategy in Primate Ventral Premotor Cortex

Vargas-Irwin, C. E., Franquemont, L., Black, M. J., Donoghue, J. P.

Journal of Neuroscience, 35(30):10888-10897, July 2015 (article)

Abstract
Neural activity in ventral premotor cortex (PMv) has been associated with the process of matching perceived objects with the motor commands needed to grasp them. It remains unclear how PMv networks can flexibly link percepts of objects affording multiple grasp options into a final desired hand action. Here, we use a relational encoding approach to track the functional state of PMv neuronal ensembles in macaque monkeys through the process of passive viewing, grip planning, and grasping movement execution. We used objects affording multiple possible grip strategies. The task included separate instructed delay periods for object presentation and grip instruction. This approach allowed us to distinguish responses elicited by the visual presentation of the objects from those associated with selecting a given motor plan for grasping. We show that PMv continuously incorporates information related to object shape and grip strategy as it becomes available, revealing a transition from a set of ensemble states initially most closely related to objects, to a new set of ensemble patterns reflecting unique object-grip combinations. These results suggest that PMv dynamically combines percepts, gradually navigating toward activity patterns associated with specific volitional actions, rather than directly mapping perceptual object properties onto categorical grip representations. Our results support the idea that PMv is part of a network that dynamically computes motor plans from perceptual information. Significance Statement: The present work demonstrates that the activity of groups of neurons in primate ventral premotor cortex reflects information related to visually presented objects, as well as the motor strategy used to grasp them, linking individual objects to multiple possible grips. PMv could provide useful control signals for neuroprosthetic assistive devices designed to interact with objects in a flexible way.

ps

publisher link DOI Project Page [BibTex]

publisher link DOI Project Page [BibTex]


Multi-view and 3D Deformable Part Models
Multi-view and 3D Deformable Part Models

Pepik, B., Stark, M., Gehler, P., Schiele, B.

Pattern Analysis and Machine Intelligence, 37(11):14, IEEE, March 2015 (article)

Abstract
As objects are inherently 3-dimensional, they have been modeled in 3D in the early days of computer vision. Due to the ambiguities arising from mapping 2D features to 3D models, 3D object representations have been neglected and 2D feature-based models are the predominant paradigm in object detection nowadays. While such models have achieved outstanding bounding box detection performance, they come with limited expressiveness, as they are clearly limited in their capability of reasoning about 3D shape or viewpoints. In this work, we bring the worlds of 3D and 2D object representations closer, by building an object detector which leverages the expressive power of 3D object representations while at the same time can be robustly matched to image evidence. To that end, we gradually extend the successful deformable part model [1] to include viewpoint information and part-level 3D geometry information, resulting in several different models with different level of expressiveness. We end up with a 3D object model, consisting of multiple object parts represented in 3D and a continuous appearance model. We experimentally verify that our models, while providing richer object hypotheses than the 2D object models, provide consistently better joint object localization and viewpoint estimation than the state-of-the-art multi-view and 3D object detectors on various benchmarks (KITTI [2], 3D object classes [3], Pascal3D+ [4], Pascal VOC 2007 [5], EPFL multi-view cars [6]).

ps

DOI Project Page [BibTex]

DOI Project Page [BibTex]


{Spike train SIMilarity Space} ({SSIMS}): A framework for single neuron and ensemble data analysis
Spike train SIMilarity Space (SSIMS): A framework for single neuron and ensemble data analysis

Vargas-Irwin, C. E., Brandman, D. M., Zimmermann, J. B., Donoghue, J. P., Black, M. J.

Neural Computation, 27(1):1-31, MIT Press, January 2015 (article)

Abstract
We present a method to evaluate the relative similarity of neural spiking patterns by combining spike train distance metrics with dimensionality reduction. Spike train distance metrics provide an estimate of similarity between activity patterns at multiple temporal resolutions. Vectors of pair-wise distances are used to represent the intrinsic relationships between multiple activity patterns at the level of single units or neuronal ensembles. Dimensionality reduction is then used to project the data into concise representations suitable for clustering analysis as well as exploratory visualization. Algorithm performance and robustness are evaluated using multielectrode ensemble activity data recorded in behaving primates. We demonstrate how Spike train SIMilarity Space (SSIMS) analysis captures the relationship between goal directions for an 8-directional reaching task and successfully segregates grasp types in a 3D grasping task in the absence of kinematic information. The algorithm enables exploration of virtually any type of neural spiking (time series) data, providing similarity-based clustering of neural activity states with minimal assumptions about potential information encoding models.

ps

pdf: publisher site pdf: author's proof DOI Project Page [BibTex]

pdf: publisher site pdf: author's proof DOI Project Page [BibTex]


no image
Active Reward Learning with a Novel Acquisition Function

Daniel, C., Kroemer, O., Viering, M., Metz, J., Peters, J.

Autonomous Robots, 39(3):389-405, 2015 (article)

am ei

link (url) DOI [BibTex]

link (url) DOI [BibTex]


Metric Regression Forests for Correspondence Estimation
Metric Regression Forests for Correspondence Estimation

Pons-Moll, G., Taylor, J., Shotton, J., Hertzmann, A., Fitzgibbon, A.

International Journal of Computer Vision, pages: 1-13, 2015 (article)

ps

springer PDF Project Page [BibTex]

springer PDF Project Page [BibTex]


no image
Learning Movement Primitive Attractor Goals and Sequential Skills from Kinesthetic Demonstrations

Manschitz, S., Kober, J., Gienger, M., Peters, J.

Robotics and Autonomous Systems, 74, Part A, pages: 97-107, 2015 (article)

am ei

link (url) DOI [BibTex]

link (url) DOI [BibTex]


no image
Bayesian Optimization for Learning Gaits under Uncertainty

Calandra, R., Seyfarth, A., Peters, J., Deisenroth, M.

Annals of Mathematics and Artificial Intelligence, pages: 1-19, 2015 (article)

am ei

DOI [BibTex]

DOI [BibTex]

2009


Valero-Cuevas, F., Hoffmann, H., Kurse, M. U., Kutch, J. J., Theodorou, E. A.

IEEE Reviews in Biomedical Engineering – (All authors have equally contributed), (2):110?135, 2009, clmc (article)

Abstract
Computational models of the neuromuscular system hold the potential to allow us to reach a deeper understanding of neuromuscular function and clinical rehabilitation by complementing experimentation. By serving as a means to distill and explore specific hypotheses, computational models emerge from prior experimental data and motivate future experimental work. Here we review computational tools used to understand neuromuscular function including musculoskeletal modeling, machine learning, control theory, and statistical model analysis. We conclude that these tools, when used in combination, have the potential to further our understanding of neuromuscular function by serving as a rigorous means to test scientific hypotheses in ways that complement and leverage experimental data.

am

link (url) [BibTex]

2009


link (url) [BibTex]


no image
On-line learning and modulation of periodic movements with nonlinear dynamical systems

Gams, A., Ijspeert, A., Schaal, S., Lenarčič, J.

Autonomous Robots, 27(1):3-23, 2009, clmc (article)

Abstract
Abstract  The paper presents a two-layered system for (1) learning and encoding a periodic signal without any knowledge on its frequency and waveform, and (2) modulating the learned periodic trajectory in response to external events. The system is used to learn periodic tasks on a humanoid HOAP-2 robot. The first layer of the system is a dynamical system responsible for extracting the fundamental frequency of the input signal, based on adaptive frequency oscillators. The second layer is a dynamical system responsible for learning of the waveform based on a built-in learning algorithm. By combining the two dynamical systems into one system we can rapidly teach new trajectories to robots without any knowledge of the frequency of the demonstration signal. The system extracts and learns only one period of the demonstration signal. Furthermore, the trajectories are robust to perturbations and can be modulated to cope with a dynamic environment. The system is computationally inexpensive, works on-line for any periodic signal, requires no additional signal processing to determine the frequency of the input signal and can be applied in parallel to multiple dimensions. Additionally, it can adapt to changes in frequency and shape, e.g. to non-stationary signals, such as hand-generated signals and human demonstrations.

am

link (url) [BibTex]

link (url) [BibTex]


no image
The SL simulation and real-time control software package

Schaal, S.

University of Southern California, Los Angeles, CA, 2009, clmc (techreport)

Abstract
SL was originally developed as a Simulation Laboratory software package to allow creating complex rigid-body dynamics simulations with minimal development times. It was meant to complement a real-time robotics setup such that robot programs could first be debugged in simulation before trying them on the actual robot. For this purpose, the motor control setup of SL was copied from our experience with real-time robot setups with vxWorks (Windriver Systems, Inc.)Ñindeed, more than 90% of the code is identical to the actual robot software, as will be explained later in detail. As a result, SL is divided into three software components: 1) the generic code that is shared by the actual robot and the simulation, 2) the robot specific code, and 3) the simulation specific code. The robot specific code is tailored to the robotic environments that we have experienced over the years, in particular towards VME-based multi-processor real-time operating systems. The simulation specific code has all the components for OpenGL graphics simulations and mimics the robot multi-processor environment in simple C-code. Importantly, SL can be used stand-alone for creating graphics an-imationsÑthe heritage from real-time robotics does not restrict the complexity of possible simulations. This technical report describes SL in detail and can serve as a manual for new users of SL.

am

link (url) [BibTex]

link (url) [BibTex]


no image
Local dimensionality reduction for non-parametric regression

Hoffman, H., Schaal, S., Vijayakumar, S.

Neural Processing Letters, 2009, clmc (article)

Abstract
Locally-weighted regression is a computationally-efficient technique for non-linear regression. However, for high-dimensional data, this technique becomes numerically brittle and computationally too expensive if many local models need to be maintained simultaneously. Thus, local linear dimensionality reduction combined with locally-weighted regression seems to be a promising solution. In this context, we review linear dimensionality-reduction methods, compare their performance on nonparametric locally-linear regression, and discuss their ability to extend to incremental learning. The considered methods belong to the following three groups: (1) reducing dimensionality only on the input data, (2) modeling the joint input-output data distribution, and (3) optimizing the correlation between projection directions and output data. Group 1 contains principal component regression (PCR); group 2 contains principal component analysis (PCA) in joint input and output space, factor analysis, and probabilistic PCA; and group 3 contains reduced rank regression (RRR) and partial least squares (PLS) regression. Among the tested methods, only group 3 managed to achieve robust performance even for a non-optimal number of components (factors or projection directions). In contrast, group 1 and 2 failed for fewer components since these methods rely on the correct estimate of the true intrinsic dimensionality. In group 3, PLS is the only method for which a computationally-efficient incremental implementation exists. Thus, PLS appears to be ideally suited as a building block for a locally-weighted regressor in which projection directions are incrementally added on the fly.

am

link (url) [BibTex]

link (url) [BibTex]


no image
The SL simulation and real-time control software package

Schaal, S.

University of Southern California, Los Angeles, CA, 2009, clmc (techreport)

Abstract
SL was originally developed as a Simulation Laboratory software package to allow creating complex rigid-body dynamics simulations with minimal development times. It was meant to complement a real-time robotics setup such that robot programs could first be debugged in simulation before trying them on the actual robot. For this purpose, the motor control setup of SL was copied from our experience with real-time robot setups with vxWorks (Windriver Systems, Inc.)â??indeed, more than 90% of the code is identical to the actual robot software, as will be explained later in detail. As a result, SL is divided into three software components: 1) the generic code that is shared by the actual robot and the simulation, 2) the robot specific code, and 3) the simulation specific code. The robot specific code is tailored to the robotic environments that we have experienced over the years, in particular towards VME-based multi-processor real-time operating systems. The simulation specific code has all the components for OpenGL graphics simulations and mimics the robot multi-processor environment in simple C-code. Importantly, SL can be used stand-alone for creating graphics an-imationsâ??the heritage from real-time robotics does not restrict the complexity of possible simulations. This technical report describes SL in detail and can serve as a manual for new users of SL.

am

link (url) [BibTex]

link (url) [BibTex]


no image
Incorporating Muscle Activation-Contraction dynamics to an optimal control framework for finger movements

Theodorou, Evangelos A., Valero-Cuevas, Francisco J.

Abstracts of Neural Control of Movement Conference (NCM 2009), 2009, clmc (article)

Abstract
Recent experimental and theoretical work [1] investigated the neural control of contact transition between motion and force during tapping with the index finger as a nonlinear optimization problem. Such transitions from motion to well-directed contact force are a fundamental part of dexterous manipulation. There are 3 alternative hypotheses of how this transition could be accomplished by the nervous system as a function of changes in direction and magnitude of the torque vector controlling the finger. These hypotheses are 1) an initial change in direction with a subsequent change in magnitude of the torque vector; 2) an initial change in magnitude with a subsequent directional change of the torque vector; and 3) a simultaneous and proportionally equal change of both direction and magnitude of the torque vector. Experimental work in [2] shows that the nervous system selects the first strategy, and in [1] we suggest that this may in fact be the optimal strategy. In [4] the framework of Iterative Linear Quadratic Optimal Regulator (ILQR) was extended to incorporate motion and force control. However, our prior simulation work assumed direct and instantaneous control of joint torques, which ignores the known delays and filtering properties of skeletal muscle. In this study, we implement an ILQR controller for a more biologically plausible biomechanical model of the index finger than [4], and add activation-contraction dynamics to the system to simulate muscle function. The planar biomechanical model includes the kinematics of the 3 joints while the applied torques are driven by activation?contraction dynamics with biologically plausible time constants [3]. In agreement with our experimental work [2], the task is to, within 500 ms, move the finger from a given resting configuration to target configuration with a desired terminal velocity. ILQR does not only stabilize the finger dynamics according to the objective function, but it also generates smooth joint space trajectories with minimal tuning and without an a-priori initial control policy (which is difficult to find for highly dimensional biomechanical systems). Furthemore, the use of this optimal control framework and the addition of activation-contraction dynamics considers the full nonlinear dynamics of the index finger and produces a sequence of postures which are compatible with experimental motion data [2]. These simulations combined with prior experimental results suggest that optimal control is a strong candidate for the generation of finger movements prior to abrupt motion-to-force transitions. This work is funded in part by grants NIH R01 0505520 and NSF EFRI-0836042 to Dr. Francisco J. Valero- Cuevas 1 Venkadesan M, Valero-Cuevas FJ. 
Effects of neuromuscular lags on controlling contact transitions. 
Philosophical Transactions of the Royal Society A: 2008. 2 Venkadesan M, Valero-Cuevas FJ. 
Neural Control of Motion-to-Force Transitions with the Fingertip. 
J. Neurosci., Feb 2008; 28: 1366 - 1373; 3 Zajac. Muscle and tendon: properties, models, scaling, and application to biomechanics and motor control. Crit Rev Biomed Eng, 17 4. Weiwei Li., Francisco Valero Cuevas: ?Linear Quadratic Optimal Control of Contact Transition with Fingertip ? ACC 2009

am

PDF [BibTex]

PDF [BibTex]


no image
On-line learning and modulation of periodic movements with nonlinear dynamical systems

Gams, A., Ijspeert, A., Schaal, S., Lenarčič, J.

Autonomous Robots, 27(1):3-23, 2009, clmc (article)

Abstract
Abstract  The paper presents a two-layered system for (1) learning and encoding a periodic signal without any knowledge on its frequency and waveform, and (2) modulating the learned periodic trajectory in response to external events. The system is used to learn periodic tasks on a humanoid HOAP-2 robot. The first layer of the system is a dynamical system responsible for extracting the fundamental frequency of the input signal, based on adaptive frequency oscillators. The second layer is a dynamical system responsible for learning of the waveform based on a built-in learning algorithm. By combining the two dynamical systems into one system we can rapidly teach new trajectories to robots without any knowledge of the frequency of the demonstration signal. The system extracts and learns only one period of the demonstration signal. Furthermore, the trajectories are robust to perturbations and can be modulated to cope with a dynamic environment. The system is computationally inexpensive, works on-line for any periodic signal, requires no additional signal processing to determine the frequency of the input signal and can be applied in parallel to multiple dimensions. Additionally, it can adapt to changes in frequency and shape, e.g. to non-stationary signals, such as hand-generated signals and human demonstrations.

am

link (url) [BibTex]

link (url) [BibTex]

2003


no image
Computational approaches to motor learning by imitation

Schaal, S., Ijspeert, A., Billard, A.

Philosophical Transaction of the Royal Society of London: Series B, Biological Sciences, 358(1431):537-547, 2003, clmc (article)

Abstract
Movement imitation requires a complex set of mechanisms that map an observed movement of a teacher onto one's own movement apparatus. Relevant problems include movement recognition, pose estimation, pose tracking, body correspondence, coordinate transformation from external to egocentric space, matching of observed against previously learned movement, resolution of redundant degrees-of-freedom that are unconstrained by the observation, suitable movement representations for imitation, modularization of motor control, etc. All of these topics by themselves are active research problems in computational and neurobiological sciences, such that their combination into a complete imitation system remains a daunting undertaking - indeed, one could argue that we need to understand the complete perception-action loop. As a strategy to untangle the complexity of imitation, this paper will examine imitation purely from a computational point of view, i.e. we will review statistical and mathematical approaches that have been suggested for tackling parts of the imitation problem, and discuss their merits, disadvantages and underlying principles. Given the focus on action recognition of other contributions in this special issue, this paper will primarily emphasize the motor side of imitation, assuming that a perceptual system has already identified important features of a demonstrated movement and created their corresponding spatial information. Based on the formalization of motor control in terms of control policies and their associated performance criteria, useful taxonomies of imitation learning can be generated that clarify different approaches and future research directions.

am

link (url) [BibTex]

2003


link (url) [BibTex]

1999


no image
Is imitation learning the route to humanoid robots?

Schaal, S.

Trends in Cognitive Sciences, 3(6):233-242, 1999, clmc (article)

Abstract
This review will focus on two recent developments in artificial intelligence and neural computation: learning from imitation and the development of humanoid robots. It will be postulated that the study of imitation learning offers a promising route to gain new insights into mechanisms of perceptual motor control that could ultimately lead to the creation of autonomous humanoid robots. This hope is justified because imitation learning channels research efforts towards three important issues: efficient motor learning, the connection between action and perception, and modular motor control in form of movement primitives. In order to make these points, first, a brief review of imitation learning will be given from the view of psychology and neuroscience. In these fields, representations and functional connections between action and perception have been explored that contribute to the understanding of motor acts of other beings. The recent discovery that some areas in the primate brain are active during both movement perception and execution provided a first idea of the possible neural basis of imitation. Secondly, computational approaches to imitation learning will be described, initially from the perspective of traditional AI and robotics, and then with a focus on neural network models and statistical learning research. Parallels and differences between biological and computational approaches to imitation will be highlighted. The review will end with an overview of current projects that actually employ imitation learning for humanoid robots.

am

link (url) [BibTex]

1999


link (url) [BibTex]