  Grasping a novel target object in constrained environments (e.g., walls,
bins, and shelves) requires intensive reasoning about grasp pose reachability
to avoid collisions with the surrounding structures. Typical 6-DoF robotic
grasping systems rely on the prior knowledge about the environment and
intensive planning computation, which is ungeneralizable and inefficient. In
contrast, we propose a novel Collision-Aware Reachability Predictor (CARP) for
6-DoF grasping systems. The CARP learns to estimate the collision-free
probabilities for grasp poses and significantly improves grasping in
challenging environments. The deep neural networks in our approach are trained
fully by self-supervision in simulation. The experiments in both simulation and
the real world show that our approach achieves more than 75% grasping rate on
novel objects in various surrounding structures. The ablation study
demonstrates the effectiveness of the CARP, which improves the 6-DoF grasping
rate by 95.7%.
