Prescient teleoperation of humanoid robots

TL;DR

This paper presents a predictive teleoperation system for humanoid robots that anticipates future commands using machine learning, enabling effective control despite delays up to two seconds.

Contribution

It introduces a novel approach where the robot predicts future commands to mitigate communication delays in teleoperation.

Findings

Successful control of a 32-DOF humanoid robot with 2-second delays

Effective execution in tasks like reaching, picking, and placing

Demonstrated robustness of predictive system in real-time scenarios

Abstract

Humanoid robots could be versatile and intuitive human avatars that operate remotely in inaccessible places: the robot could reproduce in the remote location the movements of an operator equipped with a wearable motion capture device while sending visual feedback to the operator. While substantial progress has been made on transferring ("retargeting") human motions to humanoid robots, a major problem preventing the deployment of such systems in real applications is the presence of communication delays between the human input and the feedback from the robot: even a few hundred milliseconds of delay can irreversibly disturb the operator, let alone a few seconds. To overcome these delays, we introduce a system in which a humanoid robot executes commands before it actually receives them, so that the visual feedback appears to be synchronized to the operator, whereas the robot executed the commands in the past. To do so, the robot continuously predicts future commands by querying a machine learning model that is trained on past trajectories and conditioned on the last received commands. In our experiments, an operator was able to successfully control a humanoid robot (32 degrees of freedom) with stochastic delays up to 2 seconds in several whole-body manipulation tasks, including reaching different targets, picking up a bottle, and placing a box at distinct locations.