Context-Based Meta Reinforcement Learning for Robust and Adaptable Peg-in-Hole Assembly Tasks

Autonomous assembly is an essential capability for industrial and service robots, with Peg-in-Hole (PiH) insertion being one of the core tasks. However, PiH assembly in unknown environments is still challenging due to uncertainty in task

parameters, such as the hole position and orientation, resulting from sensor noise. Although context-based meta reinforcement

learning (RL) methods have been previously presented to adapt to unknown task parameters in PiH assembly tasks, the per-

formance depends on a sample-inefficient procedure or human

demonstrations. Thus, to enhance the applicability of meta RL in real-world PiH assembly tasks, we propose to train the

agent to use information from the robot’s forward kinematics

and an uncalibrated camera. Furthermore, we improve the

applicability by efficiently adapting the meta-trained agent

to use data from force/torque sensor. Finally, we propose

an adaptation procedure for out-of-distribution tasks whose

parameters are different from the training tasks. Experiments on simulated and real robots prove that our modifications

enhance the sample efficiency during meta training, real-world adaptation performance, and generalization of the context-

based meta RL agent in PiH assembly tasks compared to

previous approaches.