Haptic Teleoperation of High-dimensional Robotic Systems Using a Feedback MPC Framework

TL;DR

This paper introduces a feedback MPC framework enabling stable and transparent haptic teleoperation of complex high-DoF robotic systems, overcoming the limitations of slow MPC update rates.

Contribution

It presents a novel feedback MPC approach that allows fast, independent operator input updates, improving teleoperation stability and transparency for high-dimensional robots.

Findings

Significantly improved teleoperation transparency and stability.

Framework maintains constraint satisfaction in control.

First bilateral teleoperation of a legged manipulator with whole-body MPC.

Abstract

Model Predictive Control (MPC) schemes have proven their efficiency in controlling high degree-of-freedom (DoF) complex robotic systems. However, they come at a high computational cost and an update rate of about tens of hertz. This relatively slow update rate hinders the possibility of stable haptic teleoperation of such systems since the slow feedback loops can cause instabilities and loss of transparency to the operator. This work presents a novel framework for transparent teleoperation of MPC-controlled complex robotic systems. In particular, we employ a feedback MPC approach and exploit its structure to account for the operator input at a fast rate which is independent of the update rate of the MPC loop itself. We demonstrate our framework on a mobile manipulator platform and show that it significantly improves haptic teleoperation's transparency and stability. We also highlight that the proposed feedback structure is constraint satisfactory and does not violate any constraints defined in the optimal control problem. To the best of our knowledge, this work is the first realization of the bilateral teleoperation of a legged manipulator using a whole-body MPC framework.