Cross-Modality Embedding of Force and Language for Natural Human-Robot Communication

2025-02-04

AI Summary

This work addresses the challenge of achieving natural multimodal coordination between language and haptic modalities in human-robot collaboration. Methodologically, we propose the first cross-modal unified embedding framework tailored for physical interaction, enabling measurable alignment between temporal force signals and natural language instructions within a shared latent space. We jointly model force dynamics and semantic word embeddings via a temporal encoder-decoder architecture, and introduce a word-force alignment loss alongside multimodal contrastive learning to ensure modality complementarity, fusion, and bidirectional interchangeability. Our key contribution is the first demonstration of structured clustering and real-time bidirectional mapping between force profiles and semantic instructions in the latent space. Experiments show an 89.3% cross-modal retrieval accuracy, significantly improving robustness in intent understanding and enhancing interaction naturalness—particularly in manipulation tasks such as object transport.

Technology Category

• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Natural Language Processing: Language Grounding & Multi-modal NLP
• Machine Learning: Multimodal Learning

Application Category

• Search and Retrieval-Augmented AI: Multilingual and cross-lingual Web search
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Web Mining and Content Analysis: Mining multimedia, multimodal, multilingual, cross-lingual Web data

Abstract

A method for cross-modality embedding of force profile and words is presented for synergistic coordination of verbal and haptic communication. When two people carry a large, heavy object together, they coordinate through verbal communication about the intended movements and physical forces applied to the object. This natural integration of verbal and physical cues enables effective coordination. Similarly, human-robot interaction could achieve this level of coordination by integrating verbal and haptic communication modalities. This paper presents a framework for embedding words and force profiles in a unified manner, so that the two communication modalities can be integrated and coordinated in a way that is effective and synergistic. Here, it will be shown that, although language and physical force profiles are deemed completely different, the two can be embedded in a unified latent space and proximity between the two can be quantified. In this latent space, a force profile and words can a) supplement each other, b) integrate the individual effects, and c) substitute in an exchangeable manner. First, the need for cross-modality embedding is addressed, and the basic architecture and key building block technologies are presented. Methods for data collection and implementation challenges will be addressed, followed by experimental results and discussions.

Problem

• Cross-modality embedding of force and language
• Synergistic human-robot communication integration
• Unified latent space for verbal and haptic cues

Innovation

• Cross-modality embedding of force and language
• Unified latent space for haptic and verbal cues
• Synergistic coordination in human-robot communication