ROLL: Visual Self-Supervised Reinforcement Learning with Object Reasoning

Understand

Current image-based reinforcement learning (RL) algorithms typically operate on the whole image without performing object-level reasoning.

• This leads to inefficient goal sampling and ineffective reward functions.
• In this paper, we improve upon previous visual self-supervised RL by incorporating object-level reasoning and occlusion reasoning.
• Specifically, we use unknown object segmentation to ignore distractors in the scene for better reward computation and goal generation; we further enable occlusion reasoning by employing a novel auxiliary loss and training scheme.