Supporting Long-Horizon Tasks in Human-Robot Collaboration by Aligning Intentions via Augmented Reality

Human-involved robot learning has made significant strides in performing everyday tasks. However, long-horizon human-robot collaborative tasks remain challenging due to ambiguous subtask goals, which we refer to as intention misalignment between the user and the robot. To address this, we propose a novel human-robot collaboration (HRC) paradigm that utilizes augmented reality (AR) as a bidirectional communication channel. This channel enables the robot to communicate its intentions for ambiguous subtasks to users and adapt based on their feedback. To operationalize these aligned intentions as actionable goals, we employ a goal-conditioned reinforcement learning model, where the goal reflects the user-aligned intention. We validate our approach in a classic pick-and-place task involving three distinct objects, where the user specifies the desired goal object and its target pose.