Goal-Conditioned Dual-Action Imitation Learning for Dexterous Dual-Arm Robot Manipulation

Long-horizon dexterous robot manipulation of deformable objects, such as banana peeling, is a problematic task because of the difficulties in object modeling and a lack of knowledge about stable and dexterous manipulation skills. This article presents a goal-conditioned dual-action deep imitation learning (DIL) approach that can learn dexterous manipulation skills using human demonstration data. Previous DIL methods map the current sensory input and reactive action, which often fails because of compounding errors in imitation learning caused by the recurrent computation of actions. The method predicts reactive action only when the precise manipulation of the target object is required (local action) and generates the entire trajectory when precise manipulation is not required (global action). This dual-action formulation effectively prevents compounding error in the imitation learning using the trajectory-based global action while responding to unexpected changes in the target object during the reactive local action. The proposed method was tested in a real dual-arm robot and successfully accomplished the banana-peeling task.

I. Introduction

Deep imitation learning (DIL), which trains the robot's behavior using human-generated demonstration data with deep neural networks, is a promising technique because it can transfer the implicit human knowledge of dexterous manipulation into a robot system without a predefined manipulation rule based on knowledge of the objects [1], [2], [3], [4].