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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.

am ics

PDF DOI Project Page [BibTex]

2016


PDF DOI Project Page [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)

am ics

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]


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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)

am ics

PDF DOI [BibTex]

PDF DOI [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


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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]

2015


arXiv [BibTex]


Gaussian Process Optimization for Self-Tuning Control
Gaussian Process Optimization for Self-Tuning Control

Marco, A.

Polytechnic University of Catalonia (BarcelonaTech), October 2015 (mastersthesis)

am ics

PDF Project Page [BibTex]

PDF Project Page [BibTex]


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Adaptive and Learning Concepts in Hydraulic Force Control

Doerr, A.

University of Stuttgart, September 2015 (mastersthesis)

am ics

[BibTex]

[BibTex]


Object Detection Using Deep Learning - Learning where to search using visual attention
Object Detection Using Deep Learning - Learning where to search using visual attention

Kloss, A.

Eberhard Karls Universität Tübingen, May 2015 (mastersthesis)

Abstract
Detecting and identifying the different objects in an image fast and reliably is an important skill for interacting with one’s environment. The main problem is that in theory, all parts of an image have to be searched for objects on many different scales to make sure that no object instance is missed. It however takes considerable time and effort to actually classify the content of a given image region and both time and computational capacities that an agent can spend on classification are limited. Humans use a process called visual attention to quickly decide which locations of an image need to be processed in detail and which can be ignored. This allows us to deal with the huge amount of visual information and to employ the capacities of our visual system efficiently. For computer vision, researchers have to deal with exactly the same problems, so learning from the behaviour of humans provides a promising way to improve existing algorithms. In the presented master’s thesis, a model is trained with eye tracking data recorded from 15 participants that were asked to search images for objects from three different categories. It uses a deep convolutional neural network to extract features from the input image that are then combined to form a saliency map. This map provides information about which image regions are interesting when searching for the given target object and can thus be used to reduce the parts of the image that have to be processed in detail. The method is based on a recent publication of Kümmerer et al., but in contrast to the original method that computes general, task independent saliency, the presented model is supposed to respond differently when searching for different target categories.

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PDF Project Page [BibTex]


Robot Arm Tracking with Random Decision Forests
Robot Arm Tracking with Random Decision Forests

Widmaier, F.

Eberhard-Karls-Universität Tübingen, May 2015 (mastersthesis)

Abstract
For grasping and manipulation with robot arms, knowing the current pose of the arm is crucial for successful controlling its motion. Often, pose estimations can be acquired from encoders inside the arm, but they can have significant inaccuracy which makes the use of additional techniques necessary. In this master thesis, a novel approach of robot arm pose estimation is presented, that works on single depth images without the need of prior foreground segmentation or other preprocessing steps. A random regression forest is used, which is trained only on synthetically generated data. The approach improves former work by Bohg et al. by considerably reducing the computational effort both at training and test time. The forest in the new method directly estimates the desired joint angles while in the former approach, the forest casts 3D position votes for the joints, which then have to be clustered and fed into an iterative inverse kinematic process to finally get the joint angles. To improve the estimation accuracy, the standard training objective of the forest training is replaced by a specialized function that makes use of a model-dependent distance metric, called DISP. Experimental results show that the specialized objective indeed improves pose estimation and it is shown that the method, despite of being trained on synthetic data only, is able to provide reasonable estimations for real data at test time.

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PDF Project Page [BibTex]

PDF Project Page [BibTex]


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Policy Search for Imitation Learning

Doerr, A.

University of Stuttgart, January 2015 (thesis)

am ics

link (url) Project Page [BibTex]

link (url) Project Page [BibTex]


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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)

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link (url) DOI [BibTex]

link (url) DOI [BibTex]


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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)

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link (url) DOI [BibTex]

link (url) DOI [BibTex]


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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)

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DOI [BibTex]

DOI [BibTex]


Exciting Engineered Passive Dynamics in a Bipedal Robot
Exciting Engineered Passive Dynamics in a Bipedal Robot

Renjewski, D., Spröwitz, A., Peekema, A., Jones, M., Hurst, J.

{IEEE Transactions on Robotics and Automation}, 31(5):1244-1251, IEEE, New York, NY, 2015 (article)

Abstract
A common approach in designing legged robots is to build fully actuated machines and control the machine dynamics entirely in soft- ware, carefully avoiding impacts and expending a lot of energy. However, these machines are outperformed by their human and animal counterparts. Animals achieve their impressive agility, efficiency, and robustness through a close integration of passive dynamics, implemented through mechanical components, and neural control. Robots can benefit from this same integrated approach, but a strong theoretical framework is required to design the passive dynamics of a machine and exploit them for control. For this framework, we use a bipedal spring–mass model, which has been shown to approximate the dynamics of human locomotion. This paper reports the first implementation of spring–mass walking on a bipedal robot. We present the use of template dynamics as a control objective exploiting the engineered passive spring–mass dynamics of the ATRIAS robot. The results highlight the benefits of combining passive dynamics with dynamics-based control and open up a library of spring–mass model-based control strategies for dynamic gait control of robots.

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link (url) DOI Project Page [BibTex]

link (url) DOI Project Page [BibTex]

2007


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Relative Entropy Policy Search

Peters, J.

CLMC Technical Report: TR-CLMC-2007-2, Computational Learning and Motor Control Lab, Los Angeles, CA, 2007, clmc (techreport)

Abstract
This technical report describes a cute idea of how to create new policy search approaches. It directly relates to the Natural Actor-Critic methods but allows the derivation of one shot solutions. Future work may include the application to interesting problems.

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PDF link (url) [BibTex]

2007


PDF link (url) [BibTex]


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The new robotics - towards human-centered machines

Schaal, S.

HFSP Journal Frontiers of Interdisciplinary Research in the Life Sciences, 1(2):115-126, 2007, clmc (article)

Abstract
Research in robotics has moved away from its primary focus on industrial applications. The New Robotics is a vision that has been developed in past years by our own university and many other national and international research instiutions and addresses how increasingly more human-like robots can live among us and take over tasks where our current society has shortcomings. Elder care, physical therapy, child education, search and rescue, and general assistance in daily life situations are some of the examples that will benefit from the New Robotics in the near future. With these goals in mind, research for the New Robotics has to embrace a broad interdisciplinary approach, ranging from traditional mathematical issues of robotics to novel issues in psychology, neuroscience, and ethics. This paper outlines some of the important research problems that will need to be resolved to make the New Robotics a reality.

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link (url) [BibTex]

link (url) [BibTex]


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Learning an Outlier-Robust Kalman Filter

Ting, J., Theodorou, E., Schaal, S.

CLMC Technical Report: TR-CLMC-2007-1, Los Angeles, CA, 2007, clmc (techreport)

Abstract
We introduce a modified Kalman filter that performs robust, real-time outlier detection, without the need for manual parameter tuning by the user. Systems that rely on high quality sensory data (for instance, robotic systems) can be sensitive to data containing outliers. The standard Kalman filter is not robust to outliers, and other variations of the Kalman filter have been proposed to overcome this issue. However, these methods may require manual parameter tuning, use of heuristics or complicated parameter estimation procedures. Our Kalman filter uses a weighted least squares-like approach by introducing weights for each data sample. A data sample with a smaller weight has a weaker contribution when estimating the current time step?s state. Using an incremental variational Expectation-Maximization framework, we learn the weights and system dynamics. We evaluate our Kalman filter algorithm on data from a robotic dog.

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PDF [BibTex]

PDF [BibTex]

2006


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Statistical Learning of LQG controllers

Theodorou, E.

Technical Report-2006-1, Computational Action and Vision Lab University of Minnesota, 2006, clmc (techreport)

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PDF [BibTex]

2006


PDF [BibTex]