Passivity-Centric Safe Reinforcement Learning for Contact-Rich Robotic Tasks

Understand

Reinforcement learning (RL) has achieved remarkable success in various robotic tasks; however, its deployment in real-world scenarios, particularly in contact-rich environments, often overlooks critical safety and stability aspects.

• Policies without passivity guarantees can result in system instability, posing risks to robots, their environments, and human operators.
• In this work, we investigate the limitations of traditional RL policies when deployed in contact-rich tasks and explore the combination of energy-based passive control with safe RL in both training and deployment to answer these challenges.
• Firstly, we reveal the discovery that standard RL policy does not satisfy stability in contact-rich scenarios.