Autonomous Robotic Arm Manipulation for Planetary Missions using Causal Machine Learning

arXiv:2403.00470v1 Announce Type: cross
Abstract: Autonomous robotic arm manipulators have the potential to make planetary exploration and in-situ resource utilization missions more time efficient and productive, as the manipulator can handle the objects itself and perform goal-specific actions. We train a manipulator to autonomously study objects of which it has no prior knowledge, such as planetary rocks. This is achieved using causal machine learning in a simulated planetary environment. Here, the manipulator interacts with objects, and classifies them based on …

abstract arm arxiv astro-ph.ep astro-ph.im autonomous cs.lg cs.ro exploration machine machine learning manipulation objects productive robotic robotic arm study train type