ARMCHAIR introduces a novel approach to human-robot collaboration, addressing challenges in predicting and adapting to human behavior. The integration of adversarial inverse reinforcement learning and model predictive control allows for optimal trajectory planning and decision-making in a mobile multi-robot system. The system operates autonomously, identifying the need for support during an exploration task without human intervention. Extensive simulations demonstrate improved performance in preventing collisions, maintaining network connectivity, and enhancing overall task efficiency.
ARMCHAIR aims to address the bottleneck in collaborative human-robot teams by combining advanced computational models of human behavior with control techniques. The architecture explicitly considers network connectivity requirements, ensuring seamless communication within the team. By leveraging machine learning models for human motion prediction and decision-making, ARMCHAIR offers a comprehensive solution for efficient collaboration between humans and robots.
The proposed method enhances coordination and task allocation in multi-robot systems supporting humans in various tasks. It provides autonomous decision-making capabilities based on real-time predictions of human behavior, enabling adaptive responses to changing scenarios. ARMCHAIR's closed-loop framework ensures continuous optimization of trajectories and decisions, leading to safe and efficient collaboration between robots and humans.
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