Reinforcement Learning of Active Vision for Manipulating Objects under Occlusions
This addresses the challenge of robotic manipulation in cluttered settings where distractors occlude objects, though it appears incremental by building on existing reinforcement learning methods.
The paper tackles the problem of training agents to manipulate objects under occlusions by jointly controlling a gripper and camera, resulting in active vision policies that outperform static camera setups in cluttered environments.
We consider artificial agents that learn to jointly control their gripperand camera in order to reinforcement learn manipulation policies in the presenceof occlusions from distractor objects. Distractors often occlude the object of in-terest and cause it to disappear from the field of view. We propose hand/eye con-trollers that learn to move the camera to keep the object within the field of viewand visible, in coordination to manipulating it to achieve the desired goal, e.g.,pushing it to a target location. We incorporate structural biases of object-centricattention within our actor-critic architectures, which our experiments suggest tobe a key for good performance. Our results further highlight the importance ofcurriculum with regards to environment difficulty. The resulting active vision /manipulation policies outperform static camera setups for a variety of clutteredenvironments.