Bridging the Sim-to-Real Gap from the Information Bottleneck Perspective

Understand

Reinforcement Learning (RL) has recently achieved remarkable success in robotic control.

• However, most works in RL operate in simulated environments where privileged knowledge (e.g., dynamics, surroundings, terrains) is readily available.
• Conversely, in real-world scenarios, robot agents usually rely solely on local states (e.g., proprioceptive feedback of robot joints) to select actions, leading to a significant sim-to-real gap.
• Existing methods address this gap by either gradually reducing the reliance on privileged knowledge or performing a two-stage policy imitation.