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
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
In this letter, we propose an algorithm for the training of neural network control policies for quadrotors. The learned control policy computes control commands directly from sensor inputs and is, hence, computationally efficient. An imitation learning algorithm produces a policy that reproduces the