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Learning to play table tennis is a challenging task for robots, due to the variety of the strokes required. Current advances in deep Reinforcement Learning (RL) have shown potential in learning the optimal strokes. However, the large amount of exploration still limits the applicability when utilizing RL in real scenarios. In this paper, we first propose a realistic simulation environment where several models are built for the balls dynamics and the robots kinematics. Instead of training an end-to-end RL model, we decompose it into two stages: the balls hitting state prediction and consequently learning the racket strokes from it. A novel policy gradient approach with TD3 backbone is proposed for the second stage. In the experiments, we show that the proposed approach significantly outperforms the existing RL methods in simulation. To cross the domain from simulation to reality, we develop an efficient retraining method and test in three real scenarios with a success rate of 98%.
We propose a model-free algorithm for learning efficient policies capable of returning table tennis balls by controlling robot joints at a rate of 100Hz. We demonstrate that evolutionary search (ES) methods acting on CNN-based policy architectures fo
Training robots with physical bodies requires developing new methods and action representations that allow the learning agents to explore the space of policies efficiently. This work studies sample-efficient learning of complex policies in the contex
Robot table tennis systems require a vision system that can track the ball position with low latency and high sampling rate. Altering the ball to simplify the tracking using for instance infrared coating changes the physics of the ball trajectory. As
We present a method for efficient learning of control policies for multiple related robotic motor skills. Our approach consists of two stages, joint training and specialization training. During the joint training stage, a neural network policy is tra
A technological revolution is occurring in the field of robotics with the data-driven deep learning technology. However, building datasets for each local robot is laborious. Meanwhile, data islands between local robots make data unable to be utilized