Symmetry-aware Reinforcement Learning for Robotic Assembly under Partial Observability with a Soft Wrist

Understand

This study tackles the representative yet challenging contact-rich peg-in-hole task of robotic assembly, using a soft wrist that can operate more safely and tolerate lower-frequency control signals than a rigid one.

• Previous studies often use a fully observable formulation, requiring external setups or estimators for the peg-to-hole pose.
• In contrast, we use a partially observable formulation and deep reinforcement learning from demonstrations to learn a memory-based agent that acts purely on haptic and proprioceptive signals.
• Moreover, previous works do not incorporate potential domain symmetry and thus must search for solutions in a bigger space.