Rebellion and Obedience: The Effects of Intention Prediction in Cooperative Handheld Robots
This work addresses the challenge of improving human-robot cooperation in manipulation tasks, but it appears incremental as it builds on existing intention prediction methods for a specific handheld robot setup.
The paper tackles the problem of enhancing cooperative task solving with handheld robots by proposing an intention prediction model based on user gaze patterns, achieving reliable accuracy up to 1.5 seconds before actions are executed. It assesses the model in an assisted pick-and-place task to explore how robot obedience or rebellion affects cooperation.
Within this work, we explore intention inference for user actions in the context of a handheld robot setup. Handheld robots share the shape and properties of handheld tools while being able to process task information and aid manipulation. Here, we propose an intention prediction model to enhance cooperative task solving. The model derives intention from the user's gaze pattern which is captured using a robot-mounted remote eye tracker. The proposed model yields real-time capabilities and reliable accuracy up to 1.5s prior to predicted actions being executed. We assess the model in an assisted pick and place task and show how the robot's intention obedience or rebellion affects the cooperation with the robot.