3 accepted papers
In this letter we address the challenge of exploration in deep reinforcement learning for robotic manipulation tasks. In sparse goal settings, an agent does not receive any positive feedback until randomly achieving the goal, which becomes infeasible for longer control sequences. Inspired by touch-b
We present HiDe, a novel hierarchical reinforcement learning architecture that successfully solves long horizon control tasks and generalizes to unseen test scenarios. Functional decomposition between planning and low-level control is achieved by explicitly separating the state-action spaces across
In this letter we propose a robotic vision task with the goal of enabling robots to execute complex assembly tasks in unstructured environments using a camera as the primary sensing device. We formulate the task as an instance of 6D pose estimation of template geometries, to which manipulation objec