Learning from Demonstration (LfD) allows novice users to teach robots through demonstrations without coding; however, such demonstrations are often suboptimal and can limit robot performance. To better support novices, we investigate the design of a feedback system that enables effective human-robot communication during demonstrations. We first conducted a focus group study (N = 9) to identify effective ways of visualizing key robot information, including joint limits, self-collisions, and manipulability. Guided by these insights, we designed an AR-based real-time feedback system and evaluated it in a between-subjects user study (N = 36) on a 7-DoF collaborative robot. Participants performed two tasks—insertion and pouring—with the second task enabling assessment of participants’ learning across tasks. Results show that real-time feedback reduced demonstration time, increased task completion rate, lowered perceived mental workload, and improved adherence to robot kinematic constraints. These findings demonstrate the effectiveness of the real-time feedback system for intuitive and effective robot teaching.

Can you move it to

This paper introduces a method that enables the parallel independent execution of trajectories for multi-robot / multi-arm systems in a shared workspace in MoveIt2. The proposed method leverages a centralized scheduler in a distributed set up to prevent collisions while the robots move independently. We argue that this approach is better suited than the state of the art (i.e., synchronous execution) for flexible/adaptive robotic tasks where the actions to be performed may vary in planning and execution time depending on sensor data (e.g., pick and place with inspection, assembly) as it is able to reduce the total execution time w.r.t. current approaches leveraging a single arm or multiple arms with synchronous motion planning.