ASID: Active Exploration for System Identification in Robotic Manipulation

arXiv:2404.12308v1 Announce Type: cross
Abstract: Model-free control strategies such as reinforcement learning have shown the ability to learn control strategies without requiring an accurate model or simulator of the world. While this is appealing due to the lack of modeling requirements, such methods can be sample inefficient, making them impractical in many real-world domains. On the other hand, model-based control techniques leveraging accurate simulators can circumvent these challenges and use a large amount of cheap simulation data to learn controllers …

arxiv cs.lg cs.ro cs.sy eess.sy exploration identification manipulation robotic robotic manipulation type