The intuitive collaboration of humans and intelligent robots (embodied AI) in
the real-world is an essential objective for many desirable applications of
robotics. Whilst there is much research regarding explicit communication, we
focus on how humans and robots interact implicitly, on motor adaptation level.
We present a real-world setup of a human-robot collaborative maze game,
designed to be non-trivial and only solvable through collaboration, by limiting
the actions to rotations of two orthogonal axes, and assigning each axes to one
player. This results in neither the human nor the agent being able to solve the
game on their own. We use deep reinforcement learning for the control of the
robotic agent, and achieve results within 30 minutes of real-world play,
without any type of pre-training. We then use this setup to perform systematic
experiments on human/agent behaviour and adaptation when co-learning a policy
for the collaborative game. We present results on how co-policy learning occurs
over time between the human and the robotic agent resulting in each
participant's agent serving as a representation of how they would play the
game. This allows us to relate a person's success when playing with different
agents than their own, by comparing the policy of the agent with that of their
own agent.

本研究通过一个协同迷宫游戏设计，用深度强化学习技术实现了人机共同协作。研究结果证明人机共同协作相互影响，在时间的推进下不断适应并建立起相应策略，这为人机智能的协作提供了可行的方案。