Object Rearrangement Using Learned Implicit Collision Functions

Understand

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 of 1 million scene/object point cloud pairs and 2 billion collision queries.