TactEx: An Explainable Multimodal Robotic Interaction Framework for Human-Like Touch and Hardness Estimation

• URL: http://arxiv.org/abs/2602.18967v1
• Date: Sat, 21 Feb 2026 22:18:17 GMT
• Title: TactEx: An Explainable Multimodal Robotic Interaction Framework for Human-Like Touch and Hardness Estimation
• Abstract summary: TactEx is a framework that unifies vision, touch, and language for human-like hardness estimation and interactive guidance.<n>We evaluate TactEx on fruit-ripeness assessment, a representative task that requires both tactile sensing and contextual understanding.
• Score: 7.33273170759158
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Accurate perception of object hardness is essential for safe and dexterous contact-rich robotic manipulation. Here, we present TactEx, an explainable multimodal robotic interaction framework that unifies vision, touch, and language for human-like hardness estimation and interactive guidance. We evaluate TactEx on fruit-ripeness assessment, a representative task that requires both tactile sensing and contextual understanding. The system fuses GelSight-Mini tactile streams with RGB observations and language prompts. A ResNet50+LSTM model estimates hardness from sequential tactile data, while a cross-modal alignment module combines visual cues with guidance from a large language model (LLM). This explainable multimodal interface allows users to distinguish ripeness levels with statistically significant class separation (p < 0.01 for all fruit pairs). For touch placement, we compare YOLO with Grounded-SAM (GSAM) and find GSAM to be more robust for fine-grained segmentation and contact-site selection. A lightweight LLM parses user instructions and produces grounded natural-language explanations linked to the tactile outputs. In end-to-end evaluations, TactEx attains 90% task success on simple user queries and generalises to novel tasks without large-scale tuning. These results highlight the promise of combining pretrained visual and tactile models with language grounding to advance explainable, human-like touch perception and decision-making in robotics.

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