A System for Traded Control Teleoperation of Manipulation Tasks using Intent Prediction from Hand Gestures

TL;DR

This paper introduces a teleoperation system that combines perception and intent prediction from hand gestures to enable autonomous robot manipulation, reducing task execution time compared to direct control methods.

Contribution

The novel system integrates object detection, pose tracking, and gesture-based intent prediction to facilitate traded control in manipulation tasks.

Findings

Intent prediction reduces task execution time

System accurately detects objects and predicts user intent

Autonomous grasping and retrieving motions improve efficiency

Abstract

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.