activity
20242026
most citedTouch100k: A Large-Scale Touch-Language-Vision Dataset for Touch-Centric Multimodal Representation

3 citations · 3 across the 2 of their papers we have counts for

collaborators

5 papers

cs.RO2026

τ: Learning Touch-Augmented Vision-Language-Action Models from Future Visual Supervision

Ning Cheng, Jinan Xu, Wanlin Li +5

Incorporating tactile sensing into Vision-Language-Action (VLA) models holds promise for contact-rich manipulation, where visual observations alone often fail to capture critical c…

cs.CV2025

SToLa: Self-Adaptive Touch-Language Framework with Tactile Commonsense Reasoning in Open-Ended Scenarios

Ning Cheng, Jinan Xu, Jialing Chen +2

This paper explores the challenges of integrating tactile sensing into intelligent systems for multimodal reasoning, particularly in enabling commonsense reasoning about the open-e…

cs.RO20243 cited

Touch100k: A Large-Scale Touch-Language-Vision Dataset for Touch-Centric Multimodal Representation

Ning Cheng, Changhao Guan, Jing Gao +7

Touch holds a pivotal position in enhancing the perceptual and interactive capabilities of both humans and robots. Despite its significance, current tactile research mainly focuses…

cs.LG2024

Transformer in Touch: A Survey

Jing Gao, Ning Cheng, Bin Fang +1

The Transformer model, initially achieving significant success in the field of natural language processing, has recently shown great potential in the application of tactile percept…

cs.CL2024

Potential and Limitations of LLMs in Capturing Structured Semantics: A Case Study on SRL

Ning Cheng, Zhaohui Yan, Ziming Wang +6

Large Language Models (LLMs) play a crucial role in capturing structured semantics to enhance language understanding, improve interpretability, and reduce bias. Nevertheless, an on…