Collision Avoidance with Differentiable Occupancy Functions in Object Rearrangement

Abstract

We address the challenge of object relocation by robots in environments where their behavior is expected to resemble that of humans. Existing methods typically learn to regress the position and orientation of objects specified by natural language commands using training data. However, these approaches do not account for physical constraints during training, often resulting in collisions between relocated objects. In this work, we introduce a collision avoidance loss based on functions that incorporate object size into the training process. Specifically, we propose a type of occupancy function in which particles are represented by a 3D Gaussian probability density function. By incorporating these functions into an additional training phase of existing models, we demonstrate a reduction in the number of collisions during rearrangement tasks. Notably, despite the decrease in collisions, the semantic structure of the relocation results is preserved.