Learning Robotic Insertion Tasks From Human Demonstration

Robotic insertion tasks often rely on delicate manual tuning due to the complexity of contact dynamics. In contrast, human is remarkably efficient in these tasks. In this context, Programming by Demonstration (PbD) has gained much traction since it shows the possibility for robots to learn new skills by observing human demonstration. However, existing PbD approaches suffer from the high cost of demonstration data collection, and low robustness to task uncertainties. In order to address these issues, we propose a new PbD-based learning framework for robotic insertion tasks. This framework includes a new demonstration data acquisition system, which replaces the expensive motion capture device with deep learning based hand pose tracking algorithm and a low-cost RGBD camera. A latent skill-guided reinforcement learning (RL) approach is also included for safe, efficient, and robust human-robot skill transfer, in which risky explorations are prevented by the reward function design and safety constraints in action space. A series of peg-hole-insertion experiments on a FANUC industrial robot are conducted to illustrate the effectiveness of the proposed approach.