One of the key issues in human-robot collaboration is the development of computational models that allow robots to predict and adapt to human behavior. Much progress has been achieved in developing such models, as well as control techniques that address the autonomy problems of motion planning and decision-making in robotics. However, the integration of computational models of human behavior with such control techniques still poses a major challenge, resulting in a bottleneck for efficient collaborative human-robot teams. In this context, we present a novel architecture for human-robot collaboration: Adaptive Robot Motion for Collaboration with Humans using Adversarial Inverse Reinforcement learning (ARMCHAIR). Our solution leverages adversarial inverse reinforcement learning and model predictive control to compute optimal trajectories and decisions for a mobile multi-robot system that collaborates with a human in an exploration task. During the mission, ARMCHAIR operates without human intervention, autonomously identifying the necessity to support and acting accordingly. Our approach also explicitly addresses the network connectivity requirement of the human-robot team. Extensive simulation-based evaluations demonstrate that ARMCHAIR allows a group of robots to safely support a simulated human in an exploration scenario, preventing collisions and network disconnections, and improving the overall performance of the task.

翻译：人机协作中的关键问题之一是开发能够使机器人预测并适应人类行为的计算模型。尽管此类模型及解决机器人运动规划与决策自主性问题的控制技术已取得显著进展，但将人类行为计算模型与这些控制技术相融合仍构成重大挑战，成为高效人机协作团队的瓶颈。为此，我们提出了一种面向人机协作的新型架构：基于对抗逆强化学习的自适应协作机器人运动控制框架（ARMCHAIR）。该方案融合对抗逆强化学习与模型预测控制，为与人类协同执行探索任务的移动多机器人系统计算最优轨迹与决策。在任务过程中，ARMCHAIR可在无需人工干预的情况下自主识别协作需求并采取相应行动。我们的方法还明确解决了人机团队的网络连通性需求。基于仿真的大规模评估表明，ARMCHAIR能使机器人团队在探索场景中安全辅助模拟人类，避免碰撞与网络断连，并提升任务整体性能。