Deep Reinforcement Learning for Adaptive Human Robotic Collaboration

Sammanfattning: Robots are expected to become an increasingly common part of most humans everyday lives. As the number of robots increase, so will also the number of human-robot interactions. For these interactions to be valuable and intuitive, new advanced robotic control policies will be necessary. Current policies often lack flexibility, rely heavily on human expertise and are often programmed for very specific use cases. A promising alternative is the use of Deep Reinforcement Learning, a family of algorithms that learn by trial and error. Following the recent success of Reinforcement Learning (RL) to areas previously considered too complex, RL has emerged as a possible method to learn Robotic Control Policies. This thesis explores the possibility of using Deep Reinforcement Learning (DRL) as a method to learn Robotic Control Policies for Human Robotic Collaboration (HRC). Specifically, it will evaluate if DRL algorithms can be used to train a robot to collaboratively balance a ball with a human along a predetermined path on a table. To evaluate if it is possible several experiments are performed in a simulator, where two robots jointly balance a ball, one emulating a human and one relying on the policy from the DRL algorithm. The experiments performed suggest that DRL can be used to enable HRC which perform equivalently or better than an emulated human performing the task alone. Further, the experiments indicate that less skilled human collaborators performance can be improved by cooperating with a DRL trained robot.