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Object Rearrangement Using Learned Implicit Collision Functions
[article]
2021
arXiv
pre-print
Robotic object rearrangement combines the skills of picking and placing objects. When object models are unavailable, typical collision-checking models may be unable to predict collisions in partial point clouds with occlusions, making generation of collision-free grasping or placement trajectories challenging. We propose a learned collision model that accepts scene and query object point clouds and predicts collisions for 6DOF object poses within the scene. We train the model on a synthetic set
arXiv:2011.10726v2
fatcat:ctrdpy66kfblfketagfrcnjviy