Data-efficient, Explainable and Safe Box Manipulation: Illustrating the Advantages of Physical Priors in Model-Predictive Control

arXiv:2303.01563v2 Announce Type: replace-cross
Abstract: Model-based RL/control have gained significant traction in robotics. Yet, these approaches often remain data-inefficient and lack the explainability of hand-engineered solutions. This makes them difficult to debug/integrate in safety-critical settings. However, in many systems, prior knowledge of environment kinematics/dynamics is available. Incorporating such priors can help address the aforementioned problems by reducing problem complexity and the need for exploration, while also facilitating the expression of the decisions taken by the agent in terms of physically …

abstract advantages arxiv box control cs.lg cs.ro data debug dynamics environment explainability however knowledge manipulation predictive prior robotics safe safety safety-critical solutions systems them type