Behavior Imitation for Manipulator Control and Grasping with Deep Reinforcement Learning

arXiv:2405.01284v1 Announce Type: cross
Abstract: The existing Motion Imitation models typically require expert data obtained through MoCap devices, but the vast amount of training data needed is difficult to acquire, necessitating substantial investments of financial resources, manpower, and time. This project combines 3D human pose estimation with reinforcement learning, proposing a novel model that simplifies Motion Imitation into a prediction problem of joint angle values in reinforcement learning. This significantly reduces the reliance on vast amounts of training data, enabling …

abstract arxiv behavior control cs.lg cs.ro data devices expert financial grasping human investments project reinforcement reinforcement learning resources through training training data type vast