Grasping occluded objects in cluttered environments is an essential component
in complex robotic manipulation tasks. In this paper, we introduce an
AffordanCE-driven Next-Best-View planning policy (ACE-NBV) that tries to find a
feasible grasp for target object via continuously observing scenes from new
viewpoints. This policy is motivated by the observation that the grasp
affordances of an occluded object can be better-measured under the view when
the view-direction are the same as the grasp view. Specifically, our method
leverages the paradigm of novel view imagery to predict the grasps affordances
under previously unobserved view, and select next observation view based on the
highest imagined grasp quality of the target object. The experimental results
in simulation and on a real robot demonstrate the effectiveness of the proposed
affordance-driven next-best-view planning policy. Project page:
https://sszxc.net/ace-nbv/.Comment: Conference on Robot Learning (CoRL) 202