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2015


Automatic LQR Tuning Based on Gaussian Process Optimization: Early Experimental Results
Automatic LQR Tuning Based on Gaussian Process Optimization: Early Experimental Results

Marco, A., Hennig, P., Bohg, J., Schaal, S., Trimpe, S.

Machine Learning in Planning and Control of Robot Motion Workshop at the IEEE/RSJ International Conference on Intelligent Robots and Systems (iROS), pages: , , Machine Learning in Planning and Control of Robot Motion Workshop, October 2015 (conference)

Abstract
This paper proposes an automatic controller tuning framework based on linear optimal control combined with Bayesian optimization. With this framework, an initial set of controller gains is automatically improved according to a pre-defined performance objective evaluated from experimental data. The underlying Bayesian optimization algorithm is Entropy Search, which represents the latent objective as a Gaussian process and constructs an explicit belief over the location of the objective minimum. This is used to maximize the information gain from each experimental evaluation. Thus, this framework shall yield improved controllers with fewer evaluations compared to alternative approaches. A seven-degree-of-freedom robot arm balancing an inverted pole is used as the experimental demonstrator. Preliminary results of a low-dimensional tuning problem highlight the method’s potential for automatic controller tuning on robotic platforms.

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PDF DOI Project Page [BibTex]

2015


PDF DOI Project Page [BibTex]


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Assessing human-human therapy kinematics for retargeting to human-robot therapy

Johnson, M. J., Christopher, S. M., Mohan, M., Mendonca, R.

In Proceedings of the IEEE International Conference on Rehabilitation Robotics (ICORR), Singapore, August 2015 (inproceedings)

Abstract
In this paper, we present experiments examining the accuracy of data collected from a Kinect sensor for capturing close interactive actions of a therapist with a patient during stroke rehabilitation. Our long-term goal is to map human-human interactions such as these patient-therapist ones onto human-robot interactions. In many robot interaction scenarios, the robot does not mimic interaction between two or more humans, which is a major part of stroke therapy. The Kinect works for functional tasks such as a reaching task where the interaction to be retargeted by the robot is minimal to none; though this data is not good for a functional task involving touching another person. We demonstrate that the noisy data from Kinect does not produce a system robust enough to be for remapping to a humanoid robot a therapit's movements when in contact with a person.

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DOI [BibTex]

DOI [BibTex]


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Toward a large-scale visuo-haptic dataset for robotic learning

Burka, A., Hu, S., Krishnan, S., Kuchenbecker, K. J., Hendricks, L. A., Gao, Y., Darrell, T.

In Proc. CVPR Workshop on the Future of Datasets in Vision, 2015 (inproceedings)

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Project Page [BibTex]

Project Page [BibTex]


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Detecting Lumps in Simulated Tissue via Palpation with a BioTac

Hui, J., Block, A., Kuchenbecker, K. J.

In Proc. IEEE World Haptics Conference, 2015, Work-in-progress paper. Poster presentation given by Hui (inproceedings)

hi

[BibTex]

[BibTex]


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Analysis of the Instrument Vibrations and Contact Forces Caused by an Expert Robotic Surgeon Doing FRS Tasks

Brown, J. D., O’Brien, C., Miyasaka, K., Dumon, K. R., Kuchenbecker, K. J.

In Proc. Hamlyn Symposium on Medical Robotics, pages: 75-76, London, England, June 2015, Poster presentation given by Brown (inproceedings)

hi

[BibTex]

[BibTex]


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Should Haptic Texture Vibrations Respond to User Force and Speed?

Culbertson, H., Kuchenbecker, K. J.

In IEEE World Haptics Conference, pages: 106 - 112, Evanston, Illinois, USA, June 2015, Oral presentation given by Culbertson (inproceedings)

hi

[BibTex]

[BibTex]


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Enabling the Baxter Robot to Play Hand-Clapping Games

Fitter, N. T., Neuburger, M., Kuchenbecker, K. J.

In Proc. IEEE World Haptics Conference, June 2015, Work-in-progress paper. Poster presentation given by Fitter (inproceedings)

hi

[BibTex]

[BibTex]


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Using IMU Data to Teach a Robot Hand-Clapping Games

Fitter, N. T., Kuchenbecker, K. J.

In Proc. IEEE Haptics Symposium, pages: 353-355, April 2015, Work-in-progress paper. Poster presentation given by Fitter (inproceedings)

hi

[BibTex]

[BibTex]


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Haptic Feedback in Transoral Robotic Surgery: A Feasibility Study

Bur, A. M., Gomez, E. D., Rassekh, C. H., Newman, J. G., Weinstein, G. S., Kuchenbecker, K. J.

In Proc. Annual Meeting of the Triological Society at COSM, April 2015, Poster presentation given by Bur (inproceedings)

hi

[BibTex]

[BibTex]


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Human Machine Interface for Dexto Eka: - The humanoid robot

Kumra, S., Mohan, M., Gupta, S., Vaswani, H.

In Proceedings of the IEEE International Conference on Robotics, Automation, Control and Embedded Systems (RACE), Chennai, India, Febuary 2015 (inproceedings)

Abstract
This paper illustrates hybrid control system of the humanoid robot, Dexto:Eka: focusing on the dependent or slave mode. Efficiency of any system depends on the fluid operation of its control system. Here, we elucidate the control of 12 DoF robotic arms and an omnidirectional mecanum wheel drive using an exo-frame, and a Graphical User Interface (GUI) and a control column. This paper comprises of algorithms, control mechanisms and overall flow of execution for the regulation of robotic arms, graphical user interface and locomotion.

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DOI [BibTex]

DOI [BibTex]


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Conception and development of Dexto:Eka: The Humanoid Robot - Part IV

Kumra, S., Mohan, M., Vaswani, H., Gupta, S.

In Proceedings of the IEEE International Conference on Robotics, Automation, Control and Embedded Systems (RACE), Febuary 2015 (inproceedings)

Abstract
This paper elucidates the fourth phase of the development of `Dexto:Eka: - The Humanoid Robot'. It lays special emphasis on the conception of the locomotion drive and the development of vision based system that aids navigation and tele-operation. The first three phases terminated with the completion of two robotic arms with six degrees of freedom each, structural development and the creation of a human machine interface that included an exo-frame, a control column and a graphical user interface. This phase also involved the enhancement of the exo-frame to a vision based system using a Kinect camera. The paper also focuses on the reasons behind choosing the locomotion drive and the benefits it has.

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DOI [BibTex]

DOI [BibTex]


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Design and Validation of a Practical Simulator for Transoral Robotic Surgery

Bur, A. M., Gomez, E. D., Chalian, A. A., Newman, J. G., Weinstein, G. S., Kuchenbecker, K. J.

In Proc. Society for Robotic Surgery Annual Meeting: Transoral Program, (T8), February 2015, Oral presentation given by Bur (inproceedings)

hi

[BibTex]

[BibTex]


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Inference of Cause and Effect with Unsupervised Inverse Regression

Sgouritsa, E., Janzing, D., Hennig, P., Schölkopf, B.

In Proceedings of the 18th International Conference on Artificial Intelligence and Statistics, 38, pages: 847-855, JMLR Workshop and Conference Proceedings, (Editors: Lebanon, G. and Vishwanathan, S.V.N.), JMLR.org, AISTATS, 2015 (inproceedings)

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Web PDF [BibTex]

Web PDF [BibTex]


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When to use which heuristic: A rational solution to the strategy selection problem

Lieder, F., Griffiths, T. L.

In Proceedings of the 37th Annual Conference of the Cognitive Science Society, 2015 (inproceedings)

Abstract
The human mind appears to be equipped with a toolbox full of cognitive strategies, but how do people decide when to use which strategy? We leverage rational metareasoning to derive a rational solution to this problem and apply it to decision making under uncertainty. The resulting theory reconciles the two poles of the debate about human rationality by proposing that people gradually learn to make rational use of fallible heuristics. We evaluate this theory against empirical data and existing accounts of strategy selection (i.e. SSL and RELACS). Our results suggest that while SSL and RELACS can explain people's ability to adapt to homogeneous environments in which all decision problems are of the same type, rational metareasoning can additionally explain people's ability to adapt to heterogeneous environments and flexibly switch strategies from one decision to the next.

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link (url) Project Page [BibTex]

link (url) Project Page [BibTex]


Probabilistic Line Searches for Stochastic Optimization
Probabilistic Line Searches for Stochastic Optimization

Mahsereci, M., Hennig, P.

In Advances in Neural Information Processing Systems 28, pages: 181-189, (Editors: C. Cortes, N.D. Lawrence, D.D. Lee, M. Sugiyama and R. Garnett), Curran Associates, Inc., 29th Annual Conference on Neural Information Processing Systems (NIPS), 2015 (inproceedings)

Abstract
In deterministic optimization, line searches are a standard tool ensuring stability and efficiency. Where only stochastic gradients are available, no direct equivalent has so far been formulated, because uncertain gradients do not allow for a strict sequence of decisions collapsing the search space. We construct a probabilistic line search by combining the structure of existing deterministic methods with notions from Bayesian optimization. Our method retains a Gaussian process surrogate of the univariate optimization objective, and uses a probabilistic belief over the Wolfe conditions to monitor the descent. The algorithm has very low computational cost, and no user-controlled parameters. Experiments show that it effectively removes the need to define a learning rate for stochastic gradient descent. [You can find the matlab research code under `attachments' below. The zip-file contains a minimal working example. The docstring in probLineSearch.m contains additional information. A more polished implementation in C++ will be published here at a later point. For comments and questions about the code please write to mmahsereci@tue.mpg.de.]

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Matlab research code link (url) [BibTex]

Matlab research code link (url) [BibTex]


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Children and Adults Differ in their Strategies for Social Learning

Lieder, F., Sim, Z. L., Hu, J. C., Griffiths, T. L., Xu, F.

In Proceedings of the 37th Annual Conference of the Cognitive Science Society, 2015 (inproceedings)

Abstract
Adults and children rely heavily on other people’s testimony. However, domains of knowledge where there is no consensus on the truth are likely to result in conflicting testimonies. Previous research has demonstrated that in these cases, learners look towards the majority opinion to make decisions. However, it remains unclear how learners evaluate social information, given that considering either the overall valence, or the number of testimonies, or both may lead to different conclusions. We therefore formalized several social learning strategies and compared them to the performance of adults and children. We find that children use different strategies than adults. This suggests that the development of social learning may involve the acquisition of cognitive strategies.

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link (url) [BibTex]

link (url) [BibTex]


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A Random Riemannian Metric for Probabilistic Shortest-Path Tractography

Hauberg, S., Schober, M., Liptrot, M., Hennig, P., Feragen, A.

In 18th International Conference on Medical Image Computing and Computer Assisted Intervention, 9349, pages: 597-604, Lecture Notes in Computer Science, MICCAI, 2015 (inproceedings)

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PDF DOI [BibTex]

PDF DOI [BibTex]


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Learning from others: Adult and child strategies in assessing conflicting ratings

Hu, J., Lieder, F., Griffiths, T. L., Xu, F.

In Biennial Meeting of the Society for Research in Child Development, Philadelphia, Pennsylvania, USA, 2015 (inproceedings)

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[BibTex]

[BibTex]


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Trajectory generation for multi-contact momentum control

Herzog, A., Rotella, N., Schaal, S., Righetti, L.

In 2015 IEEE-RAS 15th International Conference on Humanoid Robots (Humanoids), pages: 874-880, IEEE, Seoul, South Korea, 2015 (inproceedings)

Abstract
Simplified models of the dynamics such as the linear inverted pendulum model (LIPM) have proven to perform well for biped walking on flat ground. However, for more complex tasks the assumptions of these models can become limiting. For example, the LIPM does not allow for the control of contact forces independently, is limited to co-planar contacts and assumes that the angular momentum is zero. In this paper, we propose to use the full momentum equations of a humanoid robot in a trajectory optimization framework to plan its center of mass, linear and angular momentum trajectories. The model also allows for planning desired contact forces for each end-effector in arbitrary contact locations. We extend our previous results on linear quadratic regulator (LQR) design for momentum control by computing the (linearized) optimal momentum feedback law in a receding horizon fashion. The resulting desired momentum and the associated feedback law are then used in a hierarchical whole body control approach. Simulation experiments show that the approach is computationally fast and is able to generate plans for locomotion on complex terrains while demonstrating good tracking performance for the full humanoid control.

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link (url) DOI [BibTex]

link (url) DOI [BibTex]


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Utility-weighted sampling in decisions from experience

Lieder, F., Griffiths, T. L., Hsu, M.

In The 2nd Multidisciplinary Conference on Reinforcement Learning and Decision Making, 2015 (inproceedings)

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[BibTex]

[BibTex]


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Humanoid Momentum Estimation Using Sensed Contact Wrenches

Rotella, N., Herzog, A., Schaal, S., Righetti, L.

In 2015 IEEE-RAS 15th International Conference on Humanoid Robots (Humanoids), pages: 556-563, IEEE, Seoul, South Korea, 2015 (inproceedings)

Abstract
This work presents approaches for the estimation of quantities important for the control of the momentum of a humanoid robot. In contrast to previous approaches which use simplified models such as the Linear Inverted Pendulum Model, we present estimators based on the momentum dynamics of the robot. By using this simple yet dynamically-consistent model, we avoid the issues of using simplified models for estimation. We develop an estimator for the center of mass and full momentum which can be reformulated to estimate center of mass offsets as well as external wrenches applied to the robot. The observability of these estimators is investigated and their performance is evaluated in comparison to previous approaches.

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link (url) DOI [BibTex]

link (url) DOI [BibTex]

2014


Probabilistic Progress Bars
Probabilistic Progress Bars

Kiefel, M., Schuler, C., Hennig, P.

In Conference on Pattern Recognition (GCPR), 8753, pages: 331-341, Lecture Notes in Computer Science, (Editors: Jiang, X., Hornegger, J., and Koch, R.), Springer, GCPR, September 2014 (inproceedings)

Abstract
Predicting the time at which the integral over a stochastic process reaches a target level is a value of interest in many applications. Often, such computations have to be made at low cost, in real time. As an intuitive example that captures many features of this problem class, we choose progress bars, a ubiquitous element of computer user interfaces. These predictors are usually based on simple point estimators, with no error modelling. This leads to fluctuating behaviour confusing to the user. It also does not provide a distribution prediction (risk values), which are crucial for many other application areas. We construct and empirically evaluate a fast, constant cost algorithm using a Gauss-Markov process model which provides more information to the user.

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website+code pdf DOI [BibTex]

2014


website+code pdf DOI [BibTex]


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Automatic Skill Evaluation for a Needle Passing Task in Robotic Surgery

Leung, S., Kuchenbecker, K. J.

In Proc. IROS Workshop on the Role of Human Sensorimotor Control in Robotic Surgery, Chicago, Illinois, sep 2014, Poster presentation given by Kuchenbecker. Best Poster Award (inproceedings)

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[BibTex]

[BibTex]


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A Data-driven Approach to Remote Tactile Interaction: From a BioTac Sensor to Any Fingertip Cutaneous Device

Pacchierotti, C., Prattichizzo, D., Kuchenbecker, K. J.

In Haptics: Neuroscience, Devices, Modeling, and Applications, Proc. EuroHaptics, Part I, 8618, pages: 418-424, Lecture Notes in Computer Science, Springer-Verlag, Berlin Heidelberg, June 2014, Poster presentation given by Pacchierotti in Versailles, France (inproceedings)

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[BibTex]

[BibTex]


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Evaluating the BioTac’s Ability to Detect and Characterize Lumps in Simulated Tissue

Hui, J. C. T., Kuchenbecker, K. J.

In Haptics: Neuroscience, Devices, Modeling, and Applications, Proc. EuroHaptics, Part II, 8619, pages: 295-302, Lecture Notes in Computer Science, Springer-Verlag, Berlin Heidelberg, June 2014, Poster presentation given by Hui in Versailles, France (inproceedings)

hi

[BibTex]

[BibTex]


Probabilistic Solutions to Differential Equations and their Application to Riemannian Statistics
Probabilistic Solutions to Differential Equations and their Application to Riemannian Statistics

Hennig, P., Hauberg, S.

In Proceedings of the 17th International Conference on Artificial Intelligence and Statistics, 33, pages: 347-355, JMLR: Workshop and Conference Proceedings, (Editors: S Kaski and J Corander), Microtome Publishing, Brookline, MA, AISTATS, April 2014 (inproceedings)

Abstract
We study a probabilistic numerical method for the solution of both boundary and initial value problems that returns a joint Gaussian process posterior over the solution. Such methods have concrete value in the statistics on Riemannian manifolds, where non-analytic ordinary differential equations are involved in virtually all computations. The probabilistic formulation permits marginalising the uncertainty of the numerical solution such that statistics are less sensitive to inaccuracies. This leads to new Riemannian algorithms for mean value computations and principal geodesic analysis. Marginalisation also means results can be less precise than point estimates, enabling a noticeable speed-up over the state of the art. Our approach is an argument for a wider point that uncertainty caused by numerical calculations should be tracked throughout the pipeline of machine learning algorithms.

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pdf Youtube Supplements Project page link (url) [BibTex]

pdf Youtube Supplements Project page link (url) [BibTex]


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Analyzing Human High-Fives to Create an Effective High-Fiving Robot

Fitter, N. T., Kuchenbecker, K. J.

In Proc. ACM/IEEE International Conference on Human-Robot Interaction (HRI), pages: 156-157, Bielefeld, Germany, March 2014, Poster presentation given by Fitter (inproceedings)

hi

[BibTex]

[BibTex]


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Dynamic Modeling and Control of Voice-Coil Actuators for High-Fidelity Display of Haptic Vibrations

McMahan, W., Kuchenbecker, K. J.

In Proc. IEEE Haptics Symposium, pages: 115-122, Houston, Texas, USA, February 2014, Oral presentation given by Kuchenbecker (inproceedings)

hi

[BibTex]

[BibTex]


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A Wearable Device for Controlling a Robot Gripper With Fingertip Contact, Pressure, Vibrotactile, and Grip Force Feedback

Pierce, R. M., Fedalei, E. A., Kuchenbecker, K. J.

In Proc. IEEE Haptics Symposium, pages: 19-25, Houston, Texas, USA, February 2014, Oral presentation given by Pierce (inproceedings)

hi

[BibTex]

[BibTex]


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Methods for Robotic Tool-Mediated Haptic Surface Recognition

Romano, J. M., Kuchenbecker, K. J.

In Proc. IEEE Haptics Symposium, pages: 49-56, Houston, Texas, USA, February 2014, Oral presentation given by Kuchenbecker. Finalist for Best Paper Award (inproceedings)

hi

[BibTex]

[BibTex]


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One Hundred Data-Driven Haptic Texture Models and Open-Source Methods for Rendering on 3D Objects

Culbertson, H., Delgado, J. J. L., Kuchenbecker, K. J.

In Proc. IEEE Haptics Symposium, pages: 319-325, Houston, Texas, USA, February 2014, Poster presentation given by Culbertson. Finalist for Best Poster Award (inproceedings)

hi

[BibTex]

[BibTex]


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Probabilistic ODE Solvers with Runge-Kutta Means

Schober, M., Duvenaud, D., Hennig, P.

In Advances in Neural Information Processing Systems 27, pages: 739-747, (Editors: Z. Ghahramani, M. Welling, C. Cortes, N.D. Lawrence and K.Q. Weinberger), Curran Associates, Inc., 28th Annual Conference on Neural Information Processing Systems (NIPS), 2014 (inproceedings)

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Web link (url) [BibTex]

Web link (url) [BibTex]


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Active Learning of Linear Embeddings for Gaussian Processes

Garnett, R., Osborne, M., Hennig, P.

In Proceedings of the 30th Conference on Uncertainty in Artificial Intelligence, pages: 230-239, (Editors: NL Zhang and J Tian), AUAI Press , Corvallis, Oregon, UAI2014, 2014, another link: http://arxiv.org/abs/1310.6740 (inproceedings)

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PDF Web [BibTex]

PDF Web [BibTex]


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Probabilistic Shortest Path Tractography in DTI Using Gaussian Process ODE Solvers

Schober, M., Kasenburg, N., Feragen, A., Hennig, P., Hauberg, S.

In Medical Image Computing and Computer-Assisted Intervention – MICCAI 2014, Lecture Notes in Computer Science Vol. 8675, pages: 265-272, (Editors: P. Golland, N. Hata, C. Barillot, J. Hornegger and R. Howe), Springer, Heidelberg, MICCAI, 2014 (inproceedings)

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DOI [BibTex]

DOI [BibTex]


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Sampling for Inference in Probabilistic Models with Fast Bayesian Quadrature

Gunter, T., Osborne, M., Garnett, R., Hennig, P., Roberts, S.

In Advances in Neural Information Processing Systems 27, pages: 2789-2797, (Editors: Z. Ghahramani, M. Welling, C. Cortes, N.D. Lawrence and K.Q. Weinberger), Curran Associates, Inc., 28th Annual Conference on Neural Information Processing Systems (NIPS), 2014 (inproceedings)

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Web link (url) [BibTex]

Web link (url) [BibTex]


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Algorithm selection by rational metareasoning as a model of human strategy selection

Lieder, F., Plunkett, D., Hamrick, J. B., Russell, S. J., Hay, N. J., Griffiths, T. L.

In Advances in Neural Information Processing Systems 27, 2014 (inproceedings)

Abstract
Selecting the right algorithm is an important problem in computer science, because the algorithm often has to exploit the structure of the input to be efficient. The human mind faces the same challenge. Therefore, solutions to the algorithm selection problem can inspire models of human strategy selection and vice versa. Here, we view the algorithm selection problem as a special case of metareasoning and derive a solution that outperforms existing methods in sorting algorithm selection. We apply our theory to model how people choose between cognitive strategies and test its prediction in a behavioral experiment. We find that people quickly learn to adaptively choose between cognitive strategies. People's choices in our experiment are consistent with our model but inconsistent with previous theories of human strategy selection. Rational metareasoning appears to be a promising framework for reverse-engineering how people choose among cognitive strategies and translating the results into better solutions to the algorithm selection problem.

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Project Page [BibTex]

Project Page [BibTex]


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Cutaneous Feedback of Planar Fingertip Deformation and Vibration on a da Vinci Surgical Robot

Pacchierotti, C., Shirsat, P., Koehn, J. K., Prattichizzo, D., Kuchenbecker, K. J.

In Proc. IROS Workshop on the Role of Human Sensorimotor Control in Robotic Surgery, Chicago, Illinois, 2014, Poster presentation given by Koehn (inproceedings)

hi

[BibTex]

[BibTex]


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Incremental Local Gaussian Regression

Meier, F., Hennig, P., Schaal, S.

In Advances in Neural Information Processing Systems 27, pages: 972-980, (Editors: Z. Ghahramani, M. Welling, C. Cortes, N.D. Lawrence and K.Q. Weinberger), 28th Annual Conference on Neural Information Processing Systems (NIPS), 2014, clmc (inproceedings)

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PDF link (url) [BibTex]

PDF link (url) [BibTex]


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Efficient Bayesian Local Model Learning for Control

Meier, F., Hennig, P., Schaal, S.

In Proceedings of the IEEE International Conference on Intelligent Robots and Systems, pages: 2244 - 2249, IROS, 2014, clmc (inproceedings)

Abstract
Model-based control is essential for compliant controland force control in many modern complex robots, like humanoidor disaster robots. Due to many unknown and hard tomodel nonlinearities, analytical models of such robots are oftenonly very rough approximations. However, modern optimizationcontrollers frequently depend on reasonably accurate models,and degrade greatly in robustness and performance if modelerrors are too large. For a long time, machine learning hasbeen expected to provide automatic empirical model synthesis,yet so far, research has only generated feasibility studies butno learning algorithms that run reliably on complex robots.In this paper, we combine two promising worlds of regressiontechniques to generate a more powerful regression learningsystem. On the one hand, locally weighted regression techniquesare computationally efficient, but hard to tune due to avariety of data dependent meta-parameters. On the other hand,Bayesian regression has rather automatic and robust methods toset learning parameters, but becomes quickly computationallyinfeasible for big and high-dimensional data sets. By reducingthe complexity of Bayesian regression in the spirit of local modellearning through variational approximations, we arrive at anovel algorithm that is computationally efficient and easy toinitialize for robust learning. Evaluations on several datasetsdemonstrate very good learning performance and the potentialfor a general regression learning tool for robotics.

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PDF link (url) DOI [BibTex]

PDF link (url) DOI [BibTex]


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Dual Execution of Optimized Contact Interaction Trajectories

Toussaint, M., Ratliff, N., Bohg, J., Righetti, L., Englert, P., Schaal, S.

In 2014 IEEE/RSJ Conference on Intelligent Robots and Systems, pages: 47-54, IEEE, Chicago, USA, 2014 (inproceedings)

Abstract
Efficient manipulation requires contact to reduce uncertainty. The manipulation literature refers to this as funneling: a methodology for increasing reliability and robustness by leveraging haptic feedback and control of environmental interaction. However, there is a fundamental gap between traditional approaches to trajectory optimization and this concept of robustness by funneling: traditional trajectory optimizers do not discover force feedback strategies. From a POMDP perspective, these behaviors could be regarded as explicit observation actions planned to sufficiently reduce uncertainty thereby enabling a task. While we are sympathetic to the full POMDP view, solving full continuous-space POMDPs in high-dimensions is hard. In this paper, we propose an alternative approach in which trajectory optimization objectives are augmented with new terms that reward uncertainty reduction through contacts, explicitly promoting funneling. This augmentation shifts the responsibility of robustness toward the actual execution of the optimized trajectories. Directly tracing trajectories through configuration space would lose all robustness-dual execution achieves robustness by devising force controllers to reproduce the temporal interaction profile encoded in the dual solution of the optimization problem. This work introduces dual execution in depth and analyze its performance through robustness experiments in both simulation and on a real-world robotic platform.

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link (url) DOI [BibTex]

link (url) DOI [BibTex]


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The high availability of extreme events serves resource-rational decision-making

Lieder, F., Hsu, M., Griffiths, T. L.

In Proceedings of the 36th Annual Conference of the Cognitive Science Society, 2014 (inproceedings)

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[BibTex]

[BibTex]


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Balancing experiments on a torque-controlled humanoid with hierarchical inverse dynamics

Herzog, A., Righetti, L., Grimminger, F., Pastor, P., Schaal, S.

In 2014 IEEE/RSJ Conference on Intelligent Robots and Systems, pages: 981-988, IEEE, Chicago, USA, 2014 (inproceedings)

Abstract
Recently several hierarchical inverse dynamics controllers based on cascades of quadratic programs have been proposed for application on torque controlled robots. They have important theoretical benefits but have never been implemented on a torque controlled robot where model inaccuracies and real-time computation requirements can be problematic. In this contribution we present an experimental evaluation of these algorithms in the context of balance control for a humanoid robot. The presented experiments demonstrate the applicability of the approach under real robot conditions (i.e. model uncertainty, estimation errors, etc). We propose a simplification of the optimization problem that allows us to decrease computation time enough to implement it in a fast torque control loop. We implement a momentum-based balance controller which shows robust performance in face of unknown disturbances, even when the robot is standing on only one foot. In a second experiment, a tracking task is evaluated to demonstrate the performance of the controller with more complicated hierarchies. Our results show that hierarchical inverse dynamics controllers can be used for feedback control of humanoid robots and that momentum-based balance control can be efficiently implemented on a real robot.

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link (url) DOI [BibTex]

link (url) DOI [BibTex]


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Full Dynamics LQR Control of a Humanoid Robot: An Experimental Study on Balancing and Squatting

Mason, S., Righetti, L., Schaal, S.

In 2014 IEEE-RAS International Conference on Humanoid Robots, pages: 374-379, IEEE, Madrid, Spain, 2014 (inproceedings)

Abstract
Humanoid robots operating in human environments require whole-body controllers that can offer precise tracking and well-defined disturbance rejection behavior. In this contribution, we propose an experimental evaluation of a linear quadratic regulator (LQR) using a linearization of the full robot dynamics together with the contact constraints. The advantage of the controller is that it explicitly takes into account the coupling between the different joints to create optimal feedback controllers for whole-body control. We also propose a method to explicitly regulate other tasks of interest, such as the regulation of the center of mass of the robot or its angular momentum. In order to evaluate the performance of linear optimal control designs in a real-world scenario (model uncertainty, sensor noise, imperfect state estimation, etc), we test the controllers in a variety of tracking and balancing experiments on a torque controlled humanoid (e.g. balancing, split plane balancing, squatting, pushes while squatting, and balancing on a wheeled platform). The proposed control framework shows a reliable push recovery behavior competitive with more sophisticated balance controllers, rejecting impulses up to 11.7 Ns with peak forces of 650 N, with the added advantage of great computational simplicity. Furthermore, the controller is able to track squatting trajectories up to 1 Hz without relinearization, suggesting that the linearized dynamics is sufficient for significant ranges of motion.

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link (url) DOI [BibTex]

link (url) DOI [BibTex]


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Layers of Abstraction: (Neuro)computational models of learning local and global statistical regularities

Diaconescu, A., Lieder, F., Mathys, C., Stephan, K. E.

In 20th Annual Meeting of the Organization for Human Brain Mapping, 2014 (inproceedings)

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[BibTex]

[BibTex]


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State Estimation for a Humanoid Robot

Rotella, N., Bloesch, M., Righetti, L., Schaal, S.

In 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems, pages: 952-958, IEEE, Chicago, USA, 2014 (inproceedings)

Abstract
This paper introduces a framework for state estimation on a humanoid robot platform using only common proprioceptive sensors and knowledge of leg kinematics. The presented approach extends that detailed in prior work on a point-foot quadruped platform by adding the rotational constraints imposed by the humanoid's flat feet. As in previous work, the proposed Extended Kalman Filter accommodates contact switching and makes no assumptions about gait or terrain, making it applicable on any humanoid platform for use in any task. A nonlinear observability analysis is performed on both the point-foot and flat-foot filters and it is concluded that the addition of rotational constraints significantly simplifies singular cases and improves the observability characteristics of the system. Results on a simulated walking dataset demonstrate the performance gain of the flat-foot filter as well as confirm the results of the presented observability analysis.

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link (url) DOI [BibTex]

link (url) DOI [BibTex]