Model predictive impedance control with Gaussian processes for human and environment interaction

• Main Authors: Haninger, K. (Author), Hegeler, C. (Author), Peternel, L. (Author)
• Format: Article
• Language: English
• Published: Elsevier B.V. 2023
• Subjects: Disturbance rejection; Frequency domain analysis; Gaussian distribution; Gaussian noise (electronic); Gaussian processes; Gaussian Processes; Human robot interaction; Human–robot interaction; Humans-robot interactions; Impedance control; Intention detection; Model predictive; Model predictive control; Model-predictive control; Predictive control systems; Robot trajectory; Safety constraint; Task modelling; Trajectories; Uncertainty
• Online Access: View Fulltext in Publisher; View in Scopus
• ISBN: 09218890 (ISSN)
• DOI: 10.1016/j.robot.2023.104431

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. © 2023 Elsevier B.V.