Active Exploration in Bayesian Model-based Reinforcement Learning for Robot Manipulation

arXiv:2404.01867v1 Announce Type: cross
Abstract: Efficiently tackling multiple tasks within complex environment, such as those found in robot manipulation, remains an ongoing challenge in robotics and an opportunity for data-driven solutions, such as reinforcement learning (RL). Model-based RL, by building a dynamic model of the robot, enables data reuse and transfer learning between tasks with the same robot and similar environment. Furthermore, data gathering in robotics is expensive and we must rely on data efficient approaches such as model-based RL, …

abstract arxiv bayesian building challenge cs.lg cs.ro data data-driven dynamic environment exploration found manipulation multiple reinforcement reinforcement learning robot robotics robot manipulation solutions tasks type