SafePicking: Learning Safe Object Extraction via Object-Level Mapping
This addresses the challenge of robotic manipulation in cluttered environments, but it is incremental as it builds on existing object recognition and motion planning techniques.
The paper tackles the problem of safely extracting occluded target objects from piles by integrating object-level mapping with learning-based motion planning, achieving safe object extraction in both simulation and real-world evaluations using YCB objects.
Robots need object-level scene understanding to manipulate objects while reasoning about contact, support, and occlusion among objects. Given a pile of objects, object recognition and reconstruction can identify the boundary of object instances, giving important cues as to how the objects form and support the pile. In this work, we present a system, SafePicking, that integrates object-level mapping and learning-based motion planning to generate a motion that safely extracts occluded target objects from a pile. Planning is done by learning a deep Q-network that receives observations of predicted poses and a depth-based heightmap to output a motion trajectory, trained to maximize a safety metric reward. Our results show that the observation fusion of poses and depth-sensing gives both better performance and robustness to the model. We evaluate our methods using the YCB objects in both simulation and the real world, achieving safe object extraction from piles.