OpenViTac: Learning and Benchmarking Visuo-Tactile Policies in a Unified Sim-and-Real Framework

Yifan Wu; Qin Li; Nan Min; Guojin Zhong; Haoyu Zhao; Zhiyuan Li; Houze Xu; Shengqi Xu; Xingyao Lin; Zijie Diao; Zhaoxiang Liu; Shiguo Lian; Shunlin Lu; Shihao Zhao; Ziyi Ye; Zuxuan Wu; Yu-Gang Jiang

arXiv · 2610.10384

OpenViTac: Learning and Benchmarking Visuo-Tactile Policies in a Unified Sim-and-Real Framework

Yifan Wu·Qin Li·Nan Min·Guojin Zhong·Haoyu Zhao·Zhiyuan Li·Houze Xu·Shengqi Xu·Xingyao Lin·Zijie Diao·Zhaoxiang Liu·Shiguo Lian·Shunlin Lu·Shihao Zhao·Ziyi Ye·Zuxuan Wu·Yu-Gang Jiang

View original source ↗Read source document ↗Export metadata ↓

Abstract

Tactile feedback provides embodied agents with physical information beyond visual observations, enabling more reliable interaction with the real world. However, despite the rapid progress of vision-tactile-language-action (VTLA) policies, there remains a lack of unified benchmarks for evaluating tactile-enabled robot manipulation across simulation and the real world. To address this gap, we introduce OpenViTac, a visuo-tactile manipulation benchmark for evaluating robot policies across simulation and the real world. OpenViTac organizes contact-rich manipulation into four tactile-relevant capability dimensions and provides paired simulation-real-world settings for consistent evaluation of VLA, WAM, and VTLA policies. Building upon this benchmark, we investigate how different tactile representations and integration strategies affect the performance of pretrained VLA models. Correspondingly, we introduce OpenVTLA, a tactile augmentation framework that combines the best-performing representation and integration strategy. Furthermore, we leverage the paired benchmark setting to study sim-real co-training and analyze factors affecting cross-domain policy learning. Together, OpenViTac provides a unified platform for evaluating and advancing visuo-tactile robot manipulation.

Explore related subjects

cs.RO

Keep this discovery

Explore connections, maps & timelines

BibTeX RIS

Yifan Wu, Qin Li, Nan Min, Guojin Zhong, Haoyu Zhao, Zhiyuan Li, Houze Xu, Shengqi Xu, Xingyao Lin, Zijie Diao, Zhaoxiang Liu, Shiguo Lian, Shunlin Lu, Shihao Zhao, Ziyi Ye, Zuxuan Wu, Yu-Gang Jiang. 2026-10-07. OpenViTac: Learning and Benchmarking Visuo-Tactile Policies in a Unified Sim-and-Real Framework. https://arxiv.org/abs/2610.10384

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

arXiv2026

RoboFAC: A Comprehensive Framework for Robotic Failure Analysis and Correction

Vision-Language-Action (VLA) models have recently advanced robotic manipulation by translating natural-language instructions and visual observations into control actions. However, existing VLAs are primarily trained on successful expert demonstrations and lack structured supervision for failure diagnosis and recovery, limiting robustness in open-world scenarios. To address this limitation, we propose the Robotic Failure Analysis and Correction (RoboFAC) framework. We construct a large-scale failure-centric dataset comprising 9,440 erroneous manipulation trajectories and 78,623 QA pairs across 53 scenes in both simulation and real-world environments, with systematically categorized failure types. Leveraging this dataset, we develop a lightweight multimodal model specialized for task understanding, failure analysis, and failure correction, enabling efficient local deployment while remaining competitive with large proprietary models. Experimental results demonstrate that RoboFAC achieves a 34.1% higher failure analysis accuracy compared to GPT-4o. Furthermore, we integrated RoboFAC as an external supervisor in a real-world VLA control pipeline, yielding a 29.1% relative improvement across four tasks while significantly reducing latency relative to GPT-4o. These results demonstrate that RoboFAC enables systematic failure diagnosis and recovery, significantly enhancing VLA recovery capabilities. Our model and dataset are publicly available at https://github.com/MINT-SJTU/RoboFAC.

cs.RO↗

arXiv2026

Robotic Ultra-Long-Horizon Manipulation Skills via Human-guided Lifelong Code Generation

Large language models (LLMs) can translate natural-language instructions for robotic manipulation into executable code, but ambiguity, noisy generations, and limited context windows make ultra-long-horizon tasks unreliable. Closed-loop approaches that rely only on LLM feedback also struggle because LLMs have limited robotic reasoning, even when task errors are obvious to humans. Feedback is often stored in representations that generalize poorly to unseen tasks and can cause catastrophic forgetting as new corrections accumulate. We propose LYRA, a human-guided lifelong skill learning and code generation framework that distills human feedback into modular, reusable skills and incrementally extends their functionality across successive interactions while preserving previously learned behavior. External memory stores learned skills and execution examples; retrieval-augmented generation selects relevant knowledge, while user hints guide reuse when retrieval is insufficient, supporting ultra-long-horizon execution. Experiments on Ravens, Franka Kitchen, LIBERO-long, MetaWorld, and real-world tasks show a 0.93 success rate, up to 27\% higher than baselines, and a 42\% improvement in correction efficiency. LYRA also robustly solves ``build a house'', which requires planning over 20 primitives.

cs.RO↗

arXiv2026

Agentic Scene Policies

Designing or learning robot policies that generalize zero-shot across a range of language instructions and objects is a core problem in robotics. Vision-Language-Action models (VLAs) learn such policies end-to-end by repurposing existing Vision-Language Models (VLMs), but generalization to new instructions and objects remains challenging. An alternative is to implement a modular policy by leveraging an explicit VLM-based 3D scene representation and motion planning. While modular policies show strong zero-shot potential, they typically retrieve objects based on semantics without explicit spatial reasoning, severely restricting their overall grounding capabilities. They also interact with objects using basic grasping and navigation skills. In this work, we address these limitations by unifying grounding capabilities and robot skills in a single agentic action space through a scene-agent tool interface. By leveraging part-level affordances, our skills generalize across diverse objects and enable zero-shot interactions such as unplugging chargers and opening drawers. We name the resulting framework Agentic Scene Policies (ASP). Through extensive real-world experiments, we show how ASP consistently outperforms leading VLAs in the zero-shot setting. We also demonstrate the extensibility of our framework by introducing a mobile version of ASP to tackle room-level queries. See our project page (https://montrealrobotics.ca/agentic-scene-policies.github.io/) for more results.

cs.RO↗