A biomimetic approach to robot table tennis

Mülling, Katharina; Kober, Jens; Peters, Jan

doi:10.1177/1059712311419378

Cited by 89 publications

(86 citation statements)

References 34 publications

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“…For the simulated experiment, we used the ProMPs for playing table tennis. The trajectories used for training were from an analytical player [15]. In the first real-robot experiment, we demonstrated rhythmic trajectories for the toy 'Astrojax' [16] by kinesthetic teach-in.…”

Section: Experimental Evaluationmentioning

confidence: 99%

A probabilistic approach to robot trajectory generation

Paraschos

Neumann

Peters

2013

2013 13th IEEE-RAS International Conference on Humanoid Robots (Humanoids)

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Abstract-Motor Primitives (MPs) are a promising approach for the data-driven acquisition as well as for the modular and re-usable generation of movements. However, a modular control architecture with MPs is only effective if the MPs support co-activation as well as continuously blending the activation from one MP to the next. In addition, we need efficient mechanisms to adapt a MP to the current situation. Common approaches to movement primitives lack such capabilities or their implementation is based on heuristics. We present a probabilistic movement primitive approach that overcomes the limitations of existing approaches. We encode a primitive as a probability distribution over trajectories. The representation as distribution has several beneficial properties. It allows encoding a time-varying variance profile. Most importantly, it allows performing new operations -a product of distributions for the co-activation of MPs conditioning for generalizing the MP to different desired targets. We derive a feedback controller that reproduces a given trajectory distribution in closed form. We compare our approach to the existing state-of-the art and present real robot results for learning from demonstration.

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Section: Experimental Evaluationmentioning

confidence: 99%

A probabilistic approach to robot trajectory generation

Paraschos

Neumann

Peters

2013

2013 13th IEEE-RAS International Conference on Humanoid Robots (Humanoids)

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Section: Robot Table Tennismentioning

confidence: 99%

“…Thus far, none of the groups which have worked on robot table tennis ever got to the levels of a young child despite having robots that could see and move faster and more accurate than humans [17]. Likely explanations for this performance gap are (i) the human ability to predict hitting points from opponent movements and (ii) the robustness of human hitting movements [17]. In this paper, we use a Barrett WAM robot arm to play table tennis against human players.…”

Section: Robot Table Tennismentioning

confidence: 99%

“…Hence, robot table tennis has been used by many groups as a benchmark task in robotics [16,17]. Thus far, none of the groups which have worked on robot table tennis ever got to the levels of a young child despite having robots that could see and move faster and more accurate than humans [17]. Likely explanations for this performance gap are (i) the human ability to predict hitting points from opponent movements and (ii) the robustness of human hitting movements [17].…”

Section: Robot Table Tennismentioning

confidence: 99%

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Probabilistic Modeling of Human Movements for Intention Inference

Wang¹,

Deisenroth²,

Amor³

et al. 2012

Robotics: Science and Systems VIII

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Abstract-Inference of human intention may be an essential step towards understanding human actions and is hence important for realizing efficient human-robot interaction. In this paper, we propose the Intention-Driven Dynamics Model (IDDM), a latent variable model for inferring unknown human intentions. We train the model based on observed human movements/actions. We introduce an efficient approximate inference algorithm to infer the human's intention from an ongoing movement. We verify the feasibility of the IDDM in two scenarios, i.e., target inference in robot table tennis and action recognition for interactive humanoid robots. In both tasks, the IDDM achieves substantial improvements over state-of-the-art regression and classification.

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“…For example, the overall shape of table tennis forehands are very similar when the swing is adapted to varied trajectories of the incoming ball and a different targets on the opponent's court. To accomplish such behavior, the human player has learned by trial and error how the global parameters of a generic forehand need to be adapted due to changes in the situation (Mülling et al, 2010(Mülling et al, , 2011.…”

Section: Introductionmentioning

confidence: 99%

Reinforcement Learning to Adjust Parametrized Motor Primitives to New Situations

Kober

Peters

2014

Springer Tracts in Advanced Robotics

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Humans manage to adapt learned movements very quickly to new situations by generalizing learned behaviors from similar situations. In contrast, robots currently often need to re-learn the complete movement. In this paper, we propose a method that learns to generalize parametrized motor plans by adapting a small set of global parameters, called meta-parameters. We employ reinforcement learning to learn the required meta-parameters to deal with the current situation, described by states. We introduce an appropriate reinforcement learning algorithm based on a kernelized version of the reward-weighted regression. To show its feasibility, we evaluate this algorithm on a toy example and compare it to several previous approaches. Subsequently, we apply the approach to three robot tasks, i.e., the generalization of throwing movements in darts, of hitting movements in table tennis, and of throwing balls where the tasks are learned on several different real physical robots, i.e., a Barrett WAM, a BioRob, the JST-ICORP/SARCOS CBi and a Kuka KR 6.

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A biomimetic approach to robot table tennis

Cited by 89 publications

References 34 publications

A probabilistic approach to robot trajectory generation

A probabilistic approach to robot trajectory generation

Probabilistic Modeling of Human Movements for Intention Inference

Reinforcement Learning to Adjust Parametrized Motor Primitives to New Situations

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