A Semantic-Aware Framework for Safe and Intent-Integrative Assistance in Upper-Limb Exoskeletons

TL;DR

This paper presents a semantic-aware framework for upper-limb exoskeletons that integrates language models and anomaly detection to improve task understanding, safety, and adaptability in assistive scenarios.

Contribution

It introduces a novel framework combining large language models and diffusion-based anomaly detection for semantic understanding and adaptive assistance in exoskeletons.

Findings

Effective task semantic understanding and configuration

Reliable anomaly detection and real-time replanning

Enhanced safety and user alignment during assistance

Abstract

Upper-limb exoskeletons are primarily designed to provide assistive support by accurately interpreting and responding to human intentions. In home-care scenarios, exoskeletons are expected to adapt their assistive configurations based on the semantic information of the task, adjusting appropriately in accordance with the nature of the object being manipulated. However, existing solutions often lack the ability to understand task semantics or collaboratively plan actions with the user, limiting their generalizability. To address this challenge, this paper introduces a semantic-aware framework that integrates large language models into the task planning framework, enabling the delivery of safe and intent-integrative assistance. The proposed approach begins with the exoskeleton operating in transparent mode to capture the wearer's intent during object grasping. Once semantic information is extracted from the task description, the system automatically configures appropriate assistive parameters. In addition, a diffusion-based anomaly detector is used to continuously monitor the state of human-robot interaction and trigger real-time replanning in response to detected anomalies. During task execution, online trajectory refinement and impedance control are used to ensure safety and regulate human-robot interaction. Experimental results demonstrate that the proposed method effectively aligns with the wearer's cognition, adapts to semantically varying tasks, and responds reliably to anomalies.