Learning table tennis with a Mixture of Motor Primitives

Muelling, Katharina; Kober, Jens; Peters, Jan

doi:10.1109/ichr.2010.5686298

Cited by 97 publications

(63 citation statements)

References 20 publications

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“…Preliminary results of this work were presented in Muelling et al (2010) and Muelling et al (2012). In future work, we will include opponent prediction (Wang et al, 2012) in order to infer the hitting area from the hitting movement of the opponent.…”

Section: Resultsmentioning

confidence: 99%

Learning to select and generalize striking movements in robot table tennis

Mülling

Kober

Kroemer

et al. 2013

The International Journal of Robotics Research

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340

248

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Learning new motor tasks from physical interactions is an important goal for both robotics and machine learning. However, when moving beyond basic skills, most monolithic machine learning approaches fail to scale. For more complex skills, methods that are tailored for the domain of skill learning are needed. In this paper, we take the task of learning table tennis as an example and present a new framework that allows a robot to learn cooperative table tennis from physical interaction with a human. The robot first learns a set of elementary table tennis hitting movements from a human table tennis teacher by kinesthetic teach-in, which is compiled into a set of motor primitives represented by dynamical systems. The robot subsequently generalizes these movements to a wider range of situations using our mixture of motor primitives approach. The resulting policy enables the robot to select appropriate motor primitives as well as to generalize between them. Finally, the robot plays with a human table tennis partner and learns online to improve its behavior. We show that the resulting setup is capable of playing table tennis using an anthropomorphic robot arm.

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Section: Resultsmentioning

confidence: 99%

Learning to select and generalize striking movements in robot table tennis

Mülling

Kober

Kroemer

et al. 2013

The International Journal of Robotics Research

Self Cite

340

248

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“…Muelling et al [18] show how to predict a single action which is a combination of actions in the library. Leveraging our framework to also predict such action sequences which …”

Section: Discussionmentioning

confidence: 99%

Contextual Sequence Prediction with Application to Control Library Optimization

Dey¹,

Liu²,

Hebert³

et al. 2012

Robotics: Science and Systems VIII

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Abstract-Sequence optimization, where the items in a list are ordered to maximize some reward has many applications such as web advertisement placement, search, and control libraries in robotics. Previous work in sequence optimization produces a static ordering that does not take any features of the item or context of the problem into account. In this work, we propose a general approach to order the items within the sequence based on the context (e.g., perceptual information, environment description, and goals). We take a simple, efficient, reduction-based approach where the choice and order of the items is established by repeatedly learning simple classifiers or regressors for each "slot" in the sequence. Our approach leverages recent work on submodular function maximization to provide a formal regret reduction from submodular sequence optimization to simple costsensitive prediction. We apply our contextual sequence prediction algorithm to optimize control libraries and demonstrate results on two robotics problems: manipulator trajectory prediction and mobile robot path planning.

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“…Usually an entire task consists of a single motion, encoded as a movement primitive. This concept has been applied in a variety of tasks, including hitting movements in table tennis [1] and locomotion [2].…”

Section: Introductionmentioning

confidence: 99%

Probabilistic segmentation applied to an assembly task

Lioutikov

Neumann

Maeda

et al. 2015

2015 IEEE-RAS 15th International Conference on Humanoid Robots (Humanoids)

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Abstract-Movement primitives are a well established approach for encoding and executing robot movements. While the primitives themselves have been extensively researched, the concept of movement primitive libraries has not received as much attention. Libraries of movement primitives represent the skill set of an agent and can be queried and sequenced in order to solve specific tasks. The goal of this work is to segment unlabeled demonstrations into an optimal set of skills. Our novel approach segments the demonstrations while learning a probabilistic representation of movement primitives. The method differs from current approaches by taking advantage of the often neglected, mutual dependencies between the segments contained in the demonstrations and the primitives to be encoded. Therefore, improving the combined quality of both segmentation and skill learning. Furthermore, our method allows incorporating domain specific insights using heuristics, which are subsequently evaluated and assessed through probabilistic inference methods. We demonstrate our method on a real robot application, where the robot segments demonstrations of a chair assembly task into a skill library. The library is subsequently used to assemble the chair in an order not present in the demonstrations.

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Learning table tennis with a Mixture of Motor Primitives

Cited by 97 publications

References 20 publications

Learning to select and generalize striking movements in robot table tennis

Learning to select and generalize striking movements in robot table tennis

Contextual Sequence Prediction with Application to Control Library Optimization

Probabilistic segmentation applied to an assembly task

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