  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.
