COCOI: Contact-aware Online Context Inference for Generalizable Non-planar Pushing

Understand

General contact-rich manipulation problems are long-standing challenges in robotics due to the difficulty of understanding complicated contact physics.

• Deep reinforcement learning (RL) has shown great potential in solving robot manipulation tasks.
• However, existing RL policies have limited adaptability to environments with diverse dynamics properties, which is pivotal in solving many contact-rich manipulation tasks.
• In this work, we propose Contact-aware Online COntext Inference (COCOI), a deep RL method that encodes a context embedding of dynamics properties online using contact-rich interactions.