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Dexterous Robotic Manipulation Using Deep Reinforcement Learning and Knowledge Transfer for Complex Sparse Reward‐based Tasks by Qiang Wang; Francisco Roldan Sanchez; Robert McCarthy; David Cordova Bulens; Kevin McGuinness; Noel O'Connor; Manuel Wüthrich; Felix Widmaier; Stefan Bauer; Stephen J. Redmond is a Computer Science article available to read on EtoBox.

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## Abstract This paper describes a deep reinforcement learning (DRL) approach that won Phase 1 of the Real Robot Challenge (RRC) 2021, and then extends this method to a more difficult manipulation task. The RRC consisted of using a TriFinger robot to manipulate a cube along a specified positional trajectory, but with no requirement for the cube to have any specific orientation. We used a relatively simple reward function, a combination of a goal‐based sparse reward and a distance reward, in conjunction with Hindsight Experience Replay (HER) to guide the learning of the DRL agent (Deep Deterministic Policy Gradient [DDPG]). Our approach allowed our agents to acquire dexterous robotic manipulation strategies in simulation. These strategies were then deployed on the real robot and outperformed all other competition submissions, including those using more traditional robotic control techniques, in the final evaluation stage of the RRC. Here we extend this method, by modifying the task of Phase 1 of the RRC to require the robot to maintain the cube in a particular orientation, while the cube is moved along the required positional trajectory. The requirement to also orient the cube makes

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Author

Qiang Wang; Francisco Roldan Sanchez; Robert McCarthy; David Cordova Bulens; Kevin McGuinness; Noel O'Connor; Manuel Wüthrich; Felix Widmaier; Stefan Bauer; Stephen J. Redmond

Publisher

Wiley

Published

2022

Language

EN

Field

Computer Science (Physical Sciences)