Long-order operation tasks for skill reinforcement learning of residual hypernetworks

Introduction

A reinforcement learning system based on residual hypernetwork pools and real-time human demonstrations is proposed to achieve robust learning for long-order and complex arm operation tasks. This system enables the robotic arm to continuously try and explore through reinforcement learning. It first trains the basic model for grasping, and then learns different branch skills through the hypernetwork pool. Under the real-time teaching of the operator, it acquires more refined operation skills and features high robustness and a 100% success rate.

Experiment

Training

Eval

Method