arXiv AI July 7, 2026 By Kelin Yu, Haode Zhang, Harish Ravichandar, Yunhai Han, Ruohan Gao

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.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv AI.

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

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 Machine Learning

Jul 16

Tactile Modality Fusion for Vision-Language-Action Models

arXiv:2603. 14604v2 Announce Type: replace-cross Abstract: We propose TacFiLM, a lightweight modality-fusion approach that integrates visual-tactile signals into vision-language-action (VLA) models.

By Charlotte Morissette, Amin Abyaneh, Wei-Di Chang, Anas Houssaini, David Meger, Hsiu-Chin Lin, Jonathan Tremblay, Gregory Dudek

robotics fine-tuning multimodal

arXiv AI

Jun 24

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.

By Shivani Kamtikar, Chung Hee Kim, Camilla Tabasso, Tye Brady, Joshua Migdal, Taskin Padir

agents robotics

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 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

Related stories

TacCoRL: Integrating Tactile Feedback into VLA via Simulation

Inference-time Policy Steering via Vision and Touch

Tactile Modality Fusion for Vision-Language-Action Models

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

Blind Dexterous Grasping via Real2Sim2Real Tactile Policy Learning

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