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21 results (View BibTeX file of all listed publications)

2018

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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]

2018

am Anderson, M., Anderson, S., Berenz, V. A Value-Driven Eldercare Robot: Virtual and Physical Instantiations of a Case-Supported Principle-Based Behavior Paradigm Proceedings of the IEEE, pages: 1,15, October 2018 (article)

link (url) DOI [BibTex]

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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]

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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]

am Shao, L., Tian, Y., Bohg, J. ClusterNet: Instance Segmentation in RGB-D Images arXiv, September 2018, Submitted to ICRA'19 (article) Submitted

link (url) [BibTex]

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Leveraging Contact Forces for Learning to Grasp

Merzic, H., Bogdanovic, M., Kappler, D., Righetti, L., Bohg, J.

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

Abstract
Grasping objects under uncertainty remains an open problem in robotics research. This uncertainty is often due to noisy or partial observations of the object pose or shape. To enable a robot to react appropriately to unforeseen effects, it is crucial that it continuously takes sensor feedback into account. While visual feedback is important for inferring a grasp pose and reaching for an object, contact feedback offers valuable information during manipulation and grasp acquisition. In this paper, we use model-free deep reinforcement learning to synthesize control policies that exploit contact sensing to generate robust grasping under uncertainty. We demonstrate our approach on a multi-fingered hand that exhibits more complex finger coordination than the commonly used two- fingered grippers. We conduct extensive experiments in order to assess the performance of the learned policies, with and without contact sensing. While it is possible to learn grasping policies without contact sensing, our results suggest that contact feedback allows for a significant improvement of grasping robustness under object pose uncertainty and for objects with a complex shape.

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video arXiv [BibTex]

am mg Merzic, H., Bogdanovic, M., Kappler, D., Righetti, L., Bohg, J. Leveraging Contact Forces for Learning to Grasp arXiv, September 2018, Submitted to ICRA'19 (article) Submitted

video arXiv [BibTex]

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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]

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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)

am ics

arXiv (extended version) DOI Project Page [BibTex]

am ics Muehlebach, M., Trimpe, S. Distributed Event-Based State Estimation for Networked Systems: An LMI Approach IEEE Transactions on Automatic Control, 63(1):269-276, January 2018 (article)

arXiv (extended version) DOI Project Page [BibTex]

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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]

2014

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Wenn es was zu sagen gibt

(Klaus Tschira Award 2014 in Computer Science)

Trimpe, S.

Bild der Wissenschaft, pages: 20-23, November 2014, (popular science article in German) (article)

am ics

PDF Project Page [BibTex]

2014

am ics Trimpe, S. Wenn es was zu sagen gibt Bild der Wissenschaft, pages: 20-23, November 2014, (popular science article in German) (article)

PDF Project Page [BibTex]

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Robotics and Neuroscience

Floreano, Dario, Ijspeert, Auke Jan, Schaal, S.

Current Biology, 24(18):R910-R920, sep 2014 (article)

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[BibTex]

am Floreano, Dario, Ijspeert, Auke Jan, Schaal, S. Robotics and Neuroscience Current Biology, 24(18):R910-R920, sep 2014 (article)

[BibTex]

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Nonmyopic View Planning for Active Object Classification and Pose Estimation

Atanasov, N., Sankaran, B., Le Ny, J., Pappas, G., Daniilidis, K.

IEEE Transactions on Robotics, May 2014, clmc (article)

Abstract
One of the central problems in computer vision is the detection of semantically important objects and the estimation of their pose. Most of the work in object detection has been based on single image processing and its performance is limited by occlusions and ambiguity in appearance and geometry. This paper proposes an active approach to object detection by controlling the point of view of a mobile depth camera. When an initial static detection phase identifies an object of interest, several hypotheses are made about its class and orientation. The sensor then plans a sequence of viewpoints, which balances the amount of energy used to move with the chance of identifying the correct hypothesis. We formulate an active M-ary hypothesis testing problem, which includes sensor mobility, and solve it using a point-based approximate POMDP algorithm. The validity of our approach is verified through simulation and real-world experiments with the PR2 robot. The results suggest a significant improvement over static object detection

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Web pdf link (url) [BibTex]

am Atanasov, N., Sankaran, B., Le Ny, J., Pappas, G., Daniilidis, K. Nonmyopic View Planning for Active Object Classification and Pose Estimation IEEE Transactions on Robotics, May 2014, clmc (article)

Web pdf link (url) [BibTex]

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Data-Driven Grasp Synthesis - A Survey

Bohg, J., Morales, A., Asfour, T., Kragic, D.

IEEE Transactions on Robotics, 30, pages: 289 - 309, IEEE, April 2014 (article)

Abstract
We review the work on data-driven grasp synthesis and the methodologies for sampling and ranking candidate grasps. We divide the approaches into three groups based on whether they synthesize grasps for known, familiar or unknown objects. This structure allows us to identify common object representations and perceptual processes that facilitate the employed data-driven grasp synthesis technique. In the case of known objects, we concentrate on the approaches that are based on object recognition and pose estimation. In the case of familiar objects, the techniques use some form of a similarity matching to a set of previously encountered objects. Finally for the approaches dealing with unknown objects, the core part is the extraction of specific features that are indicative of good grasps. Our survey provides an overview of the different methodologies and discusses open problems in the area of robot grasping. We also draw a parallel to the classical approaches that rely on analytic formulations.

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PDF link (url) DOI Project Page [BibTex]

am Bohg, J., Morales, A., Asfour, T., Kragic, D. Data-Driven Grasp Synthesis - A Survey IEEE Transactions on Robotics, 30, pages: 289 - 309, IEEE, April 2014 (article)

PDF link (url) DOI Project Page [BibTex]

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A Limiting Property of the Matrix Exponential

Trimpe, S., D’Andrea, R.

IEEE Transactions on Automatic Control, 59(4):1105-1110, 2014 (article)

am ics

PDF DOI [BibTex]

am ics Trimpe, S., D’Andrea, R. A Limiting Property of the Matrix Exponential IEEE Transactions on Automatic Control, 59(4):1105-1110, 2014 (article)

PDF DOI [BibTex]

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Event-Based State Estimation With Variance-Based Triggering

Trimpe, S., D’Andrea, R.

IEEE Transactions on Automatic Control, 59(12):3266-3281, 2014 (article)

am ics

PDF Supplementary material DOI Project Page [BibTex]

am ics Trimpe, S., D’Andrea, R. Event-Based State Estimation With Variance-Based Triggering IEEE Transactions on Automatic Control, 59(12):3266-3281, 2014 (article)

PDF Supplementary material DOI Project Page [BibTex]

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Perspective: Intelligent Systems: Bits and Bots

Spatz, J. P., Schaal, S.

Nature, (509), 2014, clmc (article)

Abstract
What is intelligence, and can we create it? Animals can perceive, reason, react and learn, but they are just one example of an intelligent system. Intelligent systems could be robots as large as humans, helping with search-and- rescue operations in dangerous places, or smart devices as tiny as a cell, delivering drugs to a target within the body. Even computing systems can be intelligent, by perceiving the world, crawling the web and processing â??big dataâ?? to extract and learn from complex information.Understanding not only how intelligence can be reproduced, but also how to build systems that put these ideas into practice, will be a challenge. Small intelligent systems will require new materials and fabrication methods, as well as com- pact information processors and power sources. And for nano-sized systems, the rules change altogether. The laws of physics operate very differently at tiny scales: for a nanorobot, swimming through water is like struggling through treacle.Researchers at the Max Planck Institute for Intelligent Systems have begun to solve these problems by developing new computational methods, experiment- ing with unique robotic systems and fabricating tiny, artificial propellers, like bacterial flagella, to propel nanocreations through their environment.

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PDF link (url) [BibTex]

am Spatz, J. P., Schaal, S. Perspective: Intelligent Systems: Bits and Bots Nature, (509), 2014, clmc (article)

PDF link (url) [BibTex]

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An autonomous manipulation system based on force control and optimization

Righetti, L., Kalakrishnan, M., Pastor, P., Binney, J., Kelly, J., Voorhies, R. C., Sukhatme, G. S., Schaal, S.

Autonomous Robots, 36(1-2):11-30, January 2014 (article)

Abstract
In this paper we present an architecture for autonomous manipulation. Our approach is based on the belief that contact interactions during manipulation should be exploited to improve dexterity and that optimizing motion plans is useful to create more robust and repeatable manipulation behaviors. We therefore propose an architecture where state of the art force/torque control and optimization-based motion planning are the core components of the system. We give a detailed description of the modules that constitute the complete system and discuss the challenges inherent to creating such a system. We present experimental results for several grasping and manipulation tasks to demonstrate the performance and robustness of our approach.

am mg

link (url) DOI [BibTex]

am mg Righetti, L., Kalakrishnan, M., Pastor, P., Binney, J., Kelly, J., Voorhies, R. C., Sukhatme, G. S., Schaal, S. An autonomous manipulation system based on force control and optimization Autonomous Robots, 36(1-2):11-30, January 2014 (article)

link (url) DOI [BibTex]

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Learning of grasp selection based on shape-templates

Herzog, A., Pastor, P., Kalakrishnan, M., Righetti, L., Bohg, J., Asfour, T., Schaal, S.

Autonomous Robots, 36(1-2):51-65, January 2014 (article)

Abstract
The ability to grasp unknown objects still remains an unsolved problem in the robotics community. One of the challenges is to choose an appropriate grasp configuration, i.e., the 6D pose of the hand relative to the object and its finger configuration. In this paper, we introduce an algorithm that is based on the assumption that similarly shaped objects can be grasped in a similar way. It is able to synthesize good grasp poses for unknown objects by finding the best matching object shape templates associated with previously demonstrated grasps. The grasp selection algorithm is able to improve over time by using the information of previous grasp attempts to adapt the ranking of the templates to new situations. We tested our approach on two different platforms, the Willow Garage PR2 and the Barrett WAM robot, which have very different hand kinematics. Furthermore, we compared our algorithm with other grasp planners and demonstrated its superior performance. The results presented in this paper show that the algorithm is able to find good grasp configurations for a large set of unknown objects from a relatively small set of demonstrations, and does improve its performance over time.

am mg

link (url) DOI [BibTex]

2005

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Composite adaptive control with locally weighted statistical learning

Nakanishi, J., Farrell, J. A., Schaal, S.

Neural Networks, 18(1):71-90, January 2005, clmc (article)

Abstract
This paper introduces a provably stable learning adaptive control framework with statistical learning. The proposed algorithm employs nonlinear function approximation with automatic growth of the learning network according to the nonlinearities and the working domain of the control system. The unknown function in the dynamical system is approximated by piecewise linear models using a nonparametric regression technique. Local models are allocated as necessary and their parameters are optimized on-line. Inspired by composite adaptive control methods, the proposed learning adaptive control algorithm uses both the tracking error and the estimation error to update the parameters. We first discuss statistical learning of nonlinear functions, and motivate our choice of the locally weighted learning framework. Second, we begin with a class of first order SISO systems for theoretical development of our learning adaptive control framework, and present a stability proof including a parameter projection method that is needed to avoid potential singularities during adaptation. Then, we generalize our adaptive controller to higher order SISO systems, and discuss further extension to MIMO problems. Finally, we evaluate our theoretical control framework in numerical simulations to illustrate the effectiveness of the proposed learning adaptive controller for rapid convergence and high accuracy of control.

am

link (url) [BibTex]

2005

am Nakanishi, J., Farrell, J. A., Schaal, S. Composite adaptive control with locally weighted statistical learning Neural Networks, 18(1):71-90, January 2005, clmc (article)

link (url) [BibTex]

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A model of smooth pursuit based on learning of the target dynamics using only retinal signals

Shibata, T., Tabata, H., Schaal, S., Kawato, M.

Neural Networks, 18, pages: 213-225, 2005, clmc (article)

Abstract
While the predictive nature of the primate smooth pursuit system has been evident through several behavioural and neurophysiological experiments, few models have attempted to explain these results comprehensively. The model we propose in this paper in line with previous models employing optimal control theory; however, we hypothesize two new issues: (1) the medical superior temporal (MST) area in the cerebral cortex implements a recurrent neural network (RNN) in order to predict the current or future target velocity, and (2) a forward model of the target motion is acquired by on-line learning. We use stimulation studies to demonstrate how our new model supports these hypotheses.

am

link (url) [BibTex]

am Shibata, T., Tabata, H., Schaal, S., Kawato, M. A model of smooth pursuit based on learning of the target dynamics using only retinal signals Neural Networks, 18, pages: 213-225, 2005, clmc (article)

link (url) [BibTex]

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Parametric and Non-Parametric approaches for nonlinear tracking of moving objects

Hidaka, Y, Theodorou, E.

Technical Report-2005-1, 2005, clmc (article)

am

PDF [BibTex]

am Hidaka, Y, Theodorou, E. Parametric and Non-Parametric approaches for nonlinear tracking of moving objects Technical Report-2005-1, 2005, clmc (article)

PDF [BibTex]

2003

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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

am Schaal, S., Ijspeert, A., Billard, A. Computational approaches to motor learning by imitation Philosophical Transaction of the Royal Society of London: Series B, Biological Sciences, 358(1431):537-547, 2003, clmc (article)

link (url) [BibTex]

1992

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Ins CAD integrierte Kostenkalkulation (CAD-Integrated Cost Calculation)

Ehrlenspiel, K., Schaal, S.

Konstruktion 44, 12, pages: 407-414, 1992, clmc (article)

am

[BibTex]

1992

am Ehrlenspiel, K., Schaal, S. Ins CAD integrierte Kostenkalkulation (CAD-Integrated Cost Calculation) Konstruktion 44, 12, pages: 407-414, 1992, clmc (article)

[BibTex]