arXiv AI

TACTFUL: Tactile-Driven Exploration For Object Localization and Identification in Confined Environments

arXiv:2606. 24712v1 Announce Type: cross Abstract: Humans effortlessly locate and identify objects by touch alone, even without vision.

arXiv AI

Jul 7

OmniTacTune: Policy-Agnostic Real-World RL for Tactile Residual Adaptation of Visual Policies

arXiv:2607. 03723v1 Announce Type: cross Abstract: Visual policies learned from human videos, teleoperation, and robot demonstrations offer scalable motion priors, but often fail in contact-rich manipulation, where success significantly depends on local force and contact geometry.

By Kelin Yu, Haode Zhang, Harish Ravichandar, Yunhai Han, Ruohan Gao

agents reinforcement-learning robotics

arXiv AI

Jun 16

Inference-time Policy Steering via Vision and Touch

arXiv:2606. 14981v1 Announce Type: cross Abstract: Inference-time steering adapts pre-trained generative robot policies during deployment by verifying candidate actions before execution.

By Yilin Wu, Zilin Si, Zeynep Temel, Oliver Kroemer, Andrea Bajcsy

diffusion robotics multimodal

arXiv AI

Jun 11

Blind Dexterous Grasping via Real2Sim2Real Tactile Policy Learning

arXiv:2606. 11767v1 Announce Type: cross Abstract: Blind grasping with a dexterous hand is a crucial manipulation capability.

By Shengcheng Luo, Xiyan Huang, Zhe Xu, Wanlin Li, Ziyuan Jiao, Chenxi Xiao

diffusion robotics

arXiv Machine Learning

Jun 11

TacCoRL: Integrating Tactile Feedback into VLA via Simulation

arXiv:2606. 11743v1 Announce Type: cross Abstract: Vision-language-action (VLA) models provide strong visual, language, and action priors for robot manipulation, but visual observations alone often miss the local contact state required for contact-rich tasks.

By Siyu Ma, Yuqi Liang, Chang Yu, Yunuo Chen, Hao Su, Yixin Zhu, Yin Yang, Chenfanfu Jiang

reinforcement-learning robotics multimodal

arXiv AI

Jul 1

RCT: A Robot-Collected Touch-Vision-Language Dataset for Tactile Generalization

arXiv:2606. 31694v1 Announce Type: cross Abstract: For robots manipulating open-world objects, tactile representations must generalize to unseen materials.

By Jingbo He, Michael F\"arber, Roberto Calandra

rag robotics multimodal

arXiv Machine Learning

Jun 10

Dexterous Point Policy: Learning Point-based Dexterous Hand Policies from Human Demonstrations

arXiv:2606. 10614v1 Announce Type: cross Abstract: Robotic foundation models pre-trained on human demonstration videos have shown promise, but a significant embodiment gap remains when the resulting policies are deployed on real robots.

By Beomjun Kim, Seong Hyeon Park, Seunghoon Sim, Seungjun Moon, Sanghyeok Lee, Jinwoo Shin

llms agents robotics fine-tuning benchmarks

arXiv AI

Jun 12

Bounding Boxes as Goals: Language-Conditioned Grasping via Neuro-Symbolic Planning

arXiv:2606. 12910v1 Announce Type: cross Abstract: For robotics to be effectively integrated into household or industrial environments, machines must adapt to natural-language prompts in real time.

By Allison Andreyev, Landon Eum, Nestor Tiglao, Romel Gomez

llms robotics fine-tuning multimodal benchmarks

arXiv AI

Jun 11

TouchThinker: Scaling Tactile Commonsense Reasoning to the Open World with Large-scale Data and Action-aware Representation

arXiv:2606. 11637v1 Announce Type: new Abstract: Touch is a key modality for embodied agents to understand the physical world.

By Kailin Lyu, Di Wu, Pengwei Zhang, Yuhang Zheng, Yingxin Lai, Long Xiao, Kangyi Wu, Pengna Li, Chen Gao, Lianyu Hu, Xiaobin Hu, Jie Hao, Ce Hao, Weihao Yuan, Shuicheng Yan

agents robotics benchmarks

arXiv AI

Jul 1

Learning Dexterous Grasping from Sparse Taxonomy Guidance

arXiv:2604. 04138v2 Announce Type: replace-cross Abstract: Dexterous manipulation requires planning a grasp configuration suited to the object and task, which is then executed through coordinated multi-finger control.

By Juhan Park, Taerim Yoon, Seungmin Kim, Joong-Gil Kim, Wontae Ye, Jeongeun Park, Yoonbyung Chai, Geonwoo Cho, Geunwoo Cho, Dohyeong Kim, Kyungjae Lee, Yong-Jae Kim, Sungjoon Choi

reinforcement-learning robotics

arXiv AI

Jul 17

Beyond Visual Grasping: Benchmarking Complex Grasping from Detection to Execution

arXiv:2607. 14341v1 Announce Type: cross Abstract: Robust robotic grasping remains a fundamental challenge for complex real-world applications.

By Hanyi Zhang, Khang Nguyen, Charith Munasinghe, Basu Hela, Tianyu Li, Zihong Luo, Hoan Nguyen, Hans Wernher van de Venn, Yalin Zheng, Ravi Prakash, Tung D. Ta, Anh Nguyen, Baoru Huang

robotics benchmarks

arXiv AI

Jun 2

AffordGen: Generating Diverse Demonstrations for Generalizable Object Manipulation with Afford Correspondence

arXiv:2604. 10579v2 Announce Type: replace-cross Abstract: Despite the recent success of modern imitation learning methods in robot manipulation, their performance is often constrained by geometric variations due to limited data diversity.

By Jiawei Zhang, Kaizhe Hu, Yingqian Huang, Yuanchen Ju, Zhengrong Xue, Huazhe Xu

robotics

arXiv AI

Jun 24

NoContactNoWorries: Estimating Contact through Vision and Proprioception for In-Hand Dexterous Manipulation

arXiv:2606. 24450v1 Announce Type: cross Abstract: Perceiving physical contact is fundamental to dexterous manipulation.

By Soham Patil, Avirup Das, Sourabh Bhosale, Spandan Roy

llms agents reinforcement-learning robotics multimodal

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