mlsanityData to 7 Oct 2026

Cross-modality Force and Language Embeddings for Natural Human-Robot Communication

arXiv 2502.02772 ↗First posted 4 Feb 2025cs.RO cs.AI cs.HCcited by 1

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Abstract (arXiv)

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.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 8 of its 30 references that are in the map. First posted on arXiv 4 Feb 2025.

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8 of 30 references.

References · 8

  1. 2017Attention Is All You Needcited by 59,932
  2. 2021Learning Transferable Visual Models From Natural Language Supervisioncited by 33,456
  3. 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,282
  4. 2019Sentence-BERT: Sentence Embeddings using Siamese BERT-Networkscited by 9,049
  5. 2018Making Sense of Vision and Touch: Self-Supervised Learning of Multimodal Representations for Contact-Rich Taskscited by 150
  6. 2022Global Planning for Contact-Rich Manipulation via Local Smoothing of Quasi-dynamic Contact Modelscited by 46
  7. 2020Language-Conditioned Goal Generation: a New Approach to Language Grounding for RLcited by 12
  8. 2022Robust Planning for Multi-stage Forceful Manipulationcited by 4

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