ARMCHAIR: 逆強化学習とモデル予測制御を統合した人間-ロボット協調
ARMCHAIR: integrated inverse reinforcement learning and model predictive control for human-robot collaboration
敵対的逆強化学習とモデル予測制御を組み合わせ、人間の意図を推定しながら複数ロボットが探索タスクで人間を支援する協調アーキテクチャを提案した。
著者: Angelo Caregnato-Neto, Luciano Cavalcante Siebert, Arkady Zgonnikov, Marcos Ricardo Omena de Albuquerque Maximo, Rubens Junqueira Magalhães Afonso
分類: cs.RO, cs.HC, cs.MA, cs.SY, eess.SY
原文アブストラクト
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.
