Kefang Zhang

Sim2real Learning of Obstacle Avoidance for Robotic Manipulators in Uncertain Environments

Obstacle avoidance for robotic manipulators can be challenging when they operate in unstructured environments. This problem is probed with the sim-to-real (sim2real) deep reinforcement learning, such that a moving policy of the robotic arm is learnt in a simulator and then adapted to the real world.