3 accepted papers
Flexible-joint manipulators are governed by complex nonlinear dynamics, defining a challenging control problem. In this work, we propose an approach to learn an outer-loop joint trajectory tracking controller with deep reinforcement learning. The controller represented by a stochastic policy is lear…
Mobile manipulation tasks are one of the key challenges in the field of search and rescue (SAR) robotics requiring robots with flexible locomotion and manipulation abilities. Since the tasks are mostly unknown in advance, the robot has to adapt to a wide variety of terrains and workspaces during a m…
Performing manipulation with robotic arms requires a method for planning trajectories that takes multiple factors into account: collisions, joint limits, orientation constraints, torques, and duration of a trajectory. We present an approach to efficiently optimize arm trajectories with respect to mu…