Robotic Arm Manipulation with Inverse Reinforcement Learning & TD-MPC

Autor:	Hassan, Md Shoyib, Sanaullah, Sabir Md
Rok vydání:	2024
Předmět:	Computer Science - Robotics I.2.9
Druh dokumentu:	Working Paper
Popis:	One unresolved issue is how to scale model-based inverse reinforcement learning (IRL) to actual robotic manipulation tasks with unpredictable dynamics. The ability to learn from both visual and proprioceptive examples, creating algorithms that scale to high-dimensional state-spaces, and mastering strong dynamics models are the main obstacles. In this work, we provide a gradient-based inverse reinforcement learning framework that learns cost functions purely from visual human demonstrations. The shown behavior and the trajectory is then optimized using TD visual model predictive control(MPC) and the learned cost functions. We test our system using fundamental object manipulation tasks on hardware. Comment: 10 pages, 13 figures
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2407.12941 Zobrazit plný text záznamu View this record from Arxiv