Title
Model predictive impedance control with Gaussian processes for human and environment interaction
Author
Haninger, Kevin (Fraunhofer)
Hegeler, Christian (Fraunhofer)
Peternel, L. (TU Delft Human-Robot Interaction) 
Date
2023
Abstract
Robotic tasks which involve uncertainty – due to variation in goal, environment configuration, or confidence in task model – may require human input to instruct or adapt the robot. In tasks with physical contact, several existing methods for adapting robot trajectory or impedance according to individual uncertainties have been proposed, e.g., realizing intention detection or uncertainty-aware learning from demonstration. However, isolated methods cannot address the wide range of uncertainties jointly present in many tasks. To improve generality, this paper proposes a model predictive control (MPC) framework which plans both trajectory and impedance online, can consider discrete and continuous uncertainties, includes safety constraints, and can be efficiently applied to a new task. This framework can consider uncertainty from: contact constraint variation, uncertainty in human goals, or task disturbances. An uncertainty-aware task model is learned from a few (≤3) demonstrations using Gaussian Processes. This task model is used in a nonlinear MPC problem to optimize robot trajectory and impedance according to belief in discrete human goals, human kinematics, safety constraints, contact stability, and frequency-domain disturbance rejection. This MPC formulation is introduced, analyzed with respect to convexity, and validated in co-manipulation with multiple goals, a collaborative polishing task, and a collaborative assembly task.
Subject
Gaussian processes
Human–robot interaction
Impedance control
Intention detection
Model predictive control
To reference this document use:
http://resolver.tudelft.nl/uuid:8e8b61b4-0289-4d29-a65e-2a350c77306a
DOI
https://doi.org/10.1016/j.robot.2023.104431
Embargo date
2023-11-08
ISSN
0921-8890
Source
Robotics and Autonomous Systems, 165
Bibliographical note
Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.
Part of collection
Institutional Repository
Document type
journal article
Rights
© 2023 Kevin Haninger, Christian Hegeler, L. Peternel