Abstract

This paper presents a novel Stackelberg-game theoretic multilayer-online learning framework for cooperative control of nonlinear Physical Human-Robot Interaction (pHRI), where the human is modeled as the leader guiding a robot follower. This hierarchical interaction is captured as a dynamic Stackelberg game, with the human's intention estimated in real-time through online multilayer neural networks (MNNs). We introduce SVD-based weight update laws for actor-critic MNNs, which approximate value functions and control inputs for both human and robot, eliminating the need for predefined basis functions. In this framework, the human objective is first inferred and used to guide the robot actions by shaping the robot control policy. The robot, acting as the follower, then adjusts its control inputs to optimize its own performance while adhering to the safety constraints and interaction dynamics dictated by the human leader inferred objectives. By applying Karush-Kuhn-Tucker (KKT) conditions to both cost functions, we develop a two-layer control structure that maintains the hierarchical nature of HRI while ensuring safety.

Department(s)

Electrical and Computer Engineering

Second Department

Computer Science

Comments

Army Research Office, Grant W911NF-22-2-0199

Keywords and Phrases

Human Robot Interaction; Online Learning; Safety; Stackelberg Games

International Standard Serial Number (ISSN)

0743-1619

Document Type

Article - Conference proceedings

Document Version

Citation

File Type

text

Language(s)

English

Rights

© 2025 Institute of Electrical and Electronics Engineers, All rights reserved.

Publication Date

01 Jan 2025

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