---
pipeline_tag: image-text-to-text
datasets:
- laiyingxin/TouchReason
tags:
- tactile-reasoning
- multimodal
- physical-reasoning
- reinforcement-learning
- robotics
- arxiv:2605.27154
---

# Touch-R1

### Reinforcing Touch Reasoning in MLLMs

[Paper](https://arxiv.org/abs/2605.27154) · [Project Page](https://laiyingxin2.github.io/Touch-R1/) · [GitHub](https://github.com/laiyingxin2/Touch-R1) · [TouchReason Dataset](https://huggingface.co/datasets/laiyingxin/TouchReason)

**Touch-R1 brings explicit reasoning to physical contact.** It connects optical tactile observations with vision and language, enabling multimodal models to use contact evidence when judging physical properties and revising misleading visual impressions.

> **Release status:** Model card available. Model weights and verified inference instructions are coming soon. The GitHub repository currently hosts the project website; research code is forthcoming.

![Touch-R1 training overview](https://laiyingxin2.github.io/Touch-R1/assets/figures/touch_r1_overview_final.png)

## Model Summary

| Item | Description |
| :--- | :--- |
| Model type | Tactile reasoning multimodal large language model |
| Backbone reported in the paper | Qwen2.5-VL-7B |
| Research data | TouchReason-1M and TouchReason-Bench |
| Inputs | Optical tactile observations, visual context, and language questions |
| Outputs | Structured textual reasoning and physical-property judgments |

## Approach

The training pipeline combines three stages:

1. **Tactile dynamics pretraining** learns contact-sensitive representations by predicting future tactile tokens.
2. **QA supervised fine-tuning** aligns touch, vision, and language with structured reasoning.
3. **Tactile-grounded GRPO** combines ordinal-aware accuracy, cross-sensor physical consistency, and structured-format rewards with an input-side tactile grounding objective.

Responses organize evidence through `<perceive>`, `<compare>`, and `<conclude>` stages. Counterfactual tactile inputs—removed, shuffled, or noise-masked—help identify predictions that depend on genuine contact evidence.

## Research Use

Touch-R1 is designed for physical-property inference, ordinal comparison, cross-sensor reasoning, and visual–tactile conflict resolution. Its intended interface is tactile/visual question answering with structured textual judgments.

The paper reports a **60.1 average score** for Touch-R1-7B on TouchReason-Bench. See the [paper](https://arxiv.org/abs/2605.27154) for the evaluation protocol and complete results.

## Related Resources

- **Dataset:** [TouchReason](https://huggingface.co/datasets/laiyingxin/TouchReason)
- **Project page:** [Method, acquisition videos, and object atlas](https://laiyingxin2.github.io/Touch-R1/)
- **Project repository:** [Touch-R1](https://github.com/laiyingxin2/Touch-R1)

## Citation

```bibtex
@misc{lai2026touchr1reinforcingtouchreasoning,
  title         = {{Touch-R1}: Reinforcing Touch Reasoning in {MLLMs}},
  author        = {Yingxin Lai and Yafei Zhou and Fucai Zhu and Siyu Zhu and Weihao Yuan},
  year          = {2026},
  eprint        = {2605.27154},
  archivePrefix = {arXiv},
  primaryClass  = {cs.CV},
  url           = {https://arxiv.org/abs/2605.27154}
}
```