Collision-Aware Target-Driven Object Grasping in Constrained Environments

• URL: http://arxiv.org/abs/2104.00776v1
• Date: Thu, 1 Apr 2021 21:44:07 GMT
• Title: Collision-Aware Target-Driven Object Grasping in Constrained Environments
• Authors: Xibai Lou, Yang Yang and Changhyun Choi
• Abstract summary: 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 experiments in both simulation and the real world show that our approach achieves more than 75% grasping rate on novel objects.
• Score: 10.934615956723672
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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%.

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