Goal-conditioned dual-action imitation learning for dexterous dual-arm robot manipulation
This addresses the challenge of stable and dexterous manipulation for robots in handling deformable objects, representing an incremental improvement over previous imitation learning methods.
The paper tackled the problem of long-horizon dexterous robot manipulation of deformable objects like banana peeling by proposing a goal-conditioned dual-action deep imitation learning approach, which successfully accomplished the banana-peeling task on a real dual-arm robot.
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 paper presents a goal-conditioned dual-action (GC-DA) 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. Data from this and related works are available at: https://sites.google.com/view/multi-task-fine.