A System for Traded Control Teleoperation of Manipulation Tasks using Intent Prediction from Hand Gestures
This work addresses teleoperation efficiency for robotics by reducing execution time through intent prediction, though it appears incremental as it builds on existing traded control methods.
The paper tackles teleoperation of manipulation tasks by introducing a system that uses hand gestures for intent prediction to specify goal objects, enabling traded control where the robot autonomously generates motions, resulting in reduced overall task execution time compared to a direct control approach.
This paper presents a teleoperation system that includes robot perception and intent prediction from hand gestures. The perception module identifies the objects present in the robot workspace and the intent prediction module which object the user likely wants to grasp. This architecture allows the approach to rely on traded control instead of direct control: we use hand gestures to specify the goal objects for a sequential manipulation task, the robot then autonomously generates a grasping or a retrieving motion using trajectory optimization. The perception module relies on the model-based tracker to precisely track the 6D pose of the objects and makes use of a state of the art learning-based object detection and segmentation method, to initialize the tracker by automatically detecting objects in the scene. Goal objects are identified from user hand gestures using a trained a multi-layer perceptron classifier. After presenting all the components of the system and their empirical evaluation, we present experimental results comparing our pipeline to a direct traded control approach (i.e., one that does not use prediction) which shows that using intent prediction allows to bring down the overall task execution time.