Data-driven Koopman Operators for Model-based Shared Control of Human-Machine Systems

Abstract

We present a data-driven shared control algorithm that can be used to improve a human operator's control of complex dynamic machines and achieve tasks that would otherwise be challenging, or impossible, for the user on their own. Our method assumes no a priori knowledge of the system dynamics. Instead, both the dynamics and information about the user's interaction are learned from observation through the use of a Koopman operator. Using the learned model, we define an optimization problem to compute the autonomous partner's control policy. Finally, we dynamically allocate control authority to each partner based on a comparison of the user input and the autonomously generated control. We refer to this idea as model-based shared control (MbSC). We evaluate the efficacy of our approach with two human subjects studies consisting of 32 total participants (16 subjects in each study). The first study imposes a linear constraint on the modeling and autonomous policy generation algorithms. The second study explores the more general, nonlinear variant. Overall, we find that model-based shared control significantly improves task and control metrics when compared to a natural learning, or user only, control paradigm. Our experiments suggest that models learned via the Koopman operator generalize across users, indicating that it is not necessary to collect data from each individual user before providing assistance with MbSC. We also demonstrate the data-efficiency of MbSC and consequently, it's usefulness in online learning paradigms. Finally, we find that the nonlinear variant has a greater impact on a user's ability to successfully achieve a defined task than the linear variant.

References in corpus (8)

• A Data-Driven Approximation of the Koopman Operator: Extending Dynamic Mode Decomposition
• On Dynamic Mode Decomposition: Theory and Applications
• Sparsity-promoting dynamic mode decomposition
• Extracting spatial-temporal coherent patterns in large-scale neural recordings using dynamic mode decomposition
• Ergodic Exploration of Distributed Information
• Model-Based Control Using Koopman Operators
• Sequential Action Control: Closed-Form Optimal Control for Nonlinear and Nonsmooth Systems
• Dynamical System Segmentation for Information Measures in Motion

Cited by in corpus (5)

• Vehicular Applications of Koopman Operator Theory -- A Survey
• Global, Unified Representation of Heterogenous Robot Dynamics Using Composition Operators: A Koopman Direct Encoding Method
• Optimal Action-based or User Prediction-based Haptic Guidance: Can You Do Even Better?
• An Intent-based Task-aware Shared Control Framework for Intuitive Hands Free Telemanipulation
• Forming Real-World Human-Robot Cooperation for Tasks With General Goal