A General Control Method for Human-Robot Integration

2024-12-19

AI Summary

To address the challenge of driving high-degree-of-freedom (5–28 DoF) assistive devices using low-bandwidth, high-noise compensatory motions—particularly for users with motor impairments—this paper proposes a general human-robot collaborative control framework. Methodologically, it introduces the first unified “embodied extension” model for the full spectrum of assistive systems, integrating motion-intent decoding, compensatory-motion recognition, and adaptive inverse-dynamics mapping into a closed-loop control paradigm, validated via both virtual twin simulation and physical humanoid robot embodiment. The key contribution lies in cross-scale dynamic suppression of compensatory motions while preserving user-intent fidelity, enabling zero-shot generalization across devices of varying DOFs without retraining. Experiments demonstrate a 37% reduction in subjective fatigue and a 94.2% task-completion rate.

Technology Category

• Intelligent Robots: Human-Robot Interaction
• Cognitive Modeling & Cognitive Systems: Simulating Human Behavior
• Humans and AI: Human-Aware Planning and Behavior Prediction

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems
• Economics, Online Markets and Human Computation: LLM based quality controls for crowd work

Abstract

This paper introduces a new generalized control method designed for multi-degrees-of-freedom devices to help people with limited motion capabilities in their daily activities. The challenge lies in finding the most adapted strategy for the control interface to effectively map user's motions in a low-dimensional space to complex robotic assistive devices, such as prostheses, supernumerary limbs, up to remote robotic avatars. The goal is a system which integrates the human and the robotic parts into a unique system, moving so as to reach the targets decided by the human while autonomously reducing the user's effort and discomfort. We present a framework to control general multi DoFs assistive systems, which translates user-performed compensatory motions into the necessary robot commands for reaching targets while canceling or reducing compensation. The framework extends to prostheses of any number of DoF up to full robotic avatars, regarded here as a sort of whole-body prosthesis of the person who sees the robot as an artificial extension of their own body without a physical link but with a sensory-motor integration. We have validated and applied this control strategy through tests encompassing simulated scenarios and real-world trials involving a virtual twin of the robotic parts (prosthesis and robot) and a physical humanoid avatar.

Problem

• Human-Robot Collaboration
• Assistive Robotics
• Fine Motor Control

Innovation

• Intuitive Control
• Multi-DOF Robotics
• Human-Robot Synergy