Real-world human-robot collaborative reinforcement learning

Abstract

The intuitive collaboration of humans and intel-ligent robots (embodied AI) in the real-world is an essentialobjective for many desirable applications of robotics. Whilstthere is much research regarding explicit communication, wefocus on how humans and robots interact implicitly, on motoradaptation level. We present a real-world setup of a human-robot collaborative maze game, designed to be non-trivial andonly solvable through collaboration, by limiting the actions torotations of two orthogonal axes, and assigning each axes to oneplayer. This results in neither the human nor the agent beingable to solve the game on their own. We use deep reinforcementlearning for the control of the robotic agent, and achieve resultswithin 30 minutes of real-world play, without any type ofpre-training. We then use this setup to perform systematicexperiments on human/agent behaviour and adaptation whenco-learning a policy for the collaborative game. We presentresults on how co-policy learning occurs over time between thehuman and the robotic agent resulting in each participant’sagent serving as a representation of how they would play thegame. This allows us to relate a person’s success when playingwith different agents than their own, by comparing the policyof the agent with that of their own agent.