Journal ArticleDOI
A biomimetic approach to robot table tennis
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This article model the human movements involved in hitting a table tennis ball using discrete movement stages and the virtual hitting point hypothesis and presents a robot system that mimics human striking behavior.Abstract:
Playing table tennis is a difficult motor task that requires fast movements, accurate control, and adaptation to task parameters. Although human beings see and move slower than most robot systems, they significantly outperform all table tennis robots. One important reason for this higher performance is the human movement generation. In this article, we study human movements during table tennis and present a robot system that mimics human striking behavior. Our focus lies on generating hitting motions capable of adapting to variations in environmental conditions, such as changes in ball speed and position. Therefore, we model the human movements involved in hitting a table tennis ball using discrete movement stages and the virtual hitting point hypothesis. The resulting model was evaluated both in a physically realistic simulation and on a real anthropomorphic seven degrees of freedom Barrett WAMTM robot arm.read more
Citations
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Dissertation
Formation and control of optimal trajectory in human multijoint arm movement : minimum torque-change model
Journal ArticleDOI
Learning to select and generalize striking movements in robot table tennis
TL;DR: In this paper, a robot learns a set of elementary table tennis hitting movements from a human table tennis teacher by kinesthetic teach-in, which is compiled into a mixture of motor primitives represented by dynamical systems.
Journal ArticleDOI
Reinforcement learning to adjust parametrized motor primitives to new situations
TL;DR: This paper proposes a method that learns to generalize parametrized motor plans by adapting a small set of global parameters, called meta-parameters, and introduces an appropriate reinforcement learning algorithm based on a kernelized version of the reward-weighted regression.
Journal ArticleDOI
Using probabilistic movement primitives in robotics
TL;DR: A stochastic feedback controller is derived that reproduces the encoded variability of the movement and the coupling of the degrees of freedom of the robot by using a probabilistic representation.
Proceedings ArticleDOI
Quadrocopter ball juggling
TL;DR: An algorithm is developed to generate an open loop trajectory guiding the vehicle to a predicted impact point - the prediction is done by integrating forward the current position and velocity estimates from a Kalman filter.
References
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Journal ArticleDOI
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