# TacVLA: Contact-Aware Tactile Fusion for Robust Vision-Language-Action Manipulation

arXiv: https://arxiv.org/abs/2603.12665 · HTML: https://mlsanity.com/paper/2603.12665/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- Placed in Tactile, force-aware and humanoid VLA, one of 13 directions in Vision-language-action models.
- Builds on 26 of its 31 references that are in the map.
- First posted on arXiv 13 Mar 2026.

## Placed in

- Vision-language-action models / [Tactile, force-aware and humanoid VLA](https://mlsanity.com/vla/tactile-force-aware-and-humanoid-vla/)

## Builds on (most cited first, 26 of 26)

- [Sigmoid Loss for Language Image Pre-Training](https://mlsanity.com/paper/2303.15343/) (2023)
- [OpenVLA: An Open-Source Vision-Language-Action Model](https://mlsanity.com/paper/2406.09246/) (2024)
- [Diffusion Policy: Visuomotor Policy Learning via Action Diffusion](https://mlsanity.com/paper/2303.04137/) (2023)
- [π0.5: a Vision-Language-Action Model with Open-World Generalization](https://mlsanity.com/paper/2504.16054/) (2025)
- [FAST: Efficient Action Tokenization for Vision-Language-Action Models](https://mlsanity.com/paper/2501.09747/) (2025)
- [Reactive Diffusion Policy: Slow-Fast Visual-Tactile Policy Learning for Contact-Rich Manipulation](https://mlsanity.com/paper/2503.02881/) (2025)
- [ForceVLA: Enhancing VLA Models with a Force-aware MoE for Contact-rich Manipulation](https://mlsanity.com/paper/2505.22159/) (2025)
- [3D-ViTac: Learning Fine-Grained Manipulation with Visuo-Tactile Sensing](https://mlsanity.com/paper/2410.24091/) (2024)
- [VTLA: Vision-Tactile-Language-Action Model with Preference Learning for Insertion Manipulation](https://mlsanity.com/paper/2505.09577/) (2025)
- [TLA: Tactile-Language-Action Model for Contact-Rich Manipulation](https://mlsanity.com/paper/2503.08548/) (2025)
- [VLA-Touch: Enhancing Vision-Language-Action Models with Dual-Level Tactile Feedback](https://mlsanity.com/paper/2507.17294/) (2025)
- [OmniVTLA: Vision-Tactile-Language-Action Models with Semantic-Aligned Tactile Sensing](https://mlsanity.com/paper/2508.08706/) (2025)
- [GelSight Wedge: Measuring High-Resolution 3D Contact Geometry with a Compact Robot Finger](https://mlsanity.com/paper/2106.08851/) (2021)
- [MimicTouch: Leveraging Multi-modal Human Tactile Demonstrations for Contact-rich Manipulation](https://mlsanity.com/paper/2310.16917/) (2023)
- [MLA: A Multisensory Language-Action Model for Multimodal Understanding and Forecasting in Robotic Manipulation](https://mlsanity.com/paper/2509.26642/) (2025)
- [UniT: Data Efficient Tactile Representation with Generalization to Unseen Objects](https://mlsanity.com/paper/2408.06481/) (2024)
- [A Survey on Imitation Learning for Contact-Rich Tasks in Robotics](https://mlsanity.com/paper/2506.13498/) (2025)
- [Vysics: Object Reconstruction Under Occlusion by Fusing Vision and Contact-Rich Physics](https://mlsanity.com/paper/2504.18719/) (2025)
- [Demonstrating the Octopi-1.5 Visual-Tactile-Language Model](https://mlsanity.com/paper/2507.09985/) (2025)
- [CompliantVLA-adaptor: VLM-Guided Variable Impedance Action for Safe Contact-Rich Manipulation](https://mlsanity.com/paper/2601.15541/) (2026)
- [Canonical Policy: Learning Canonical 3D Representation for SE(3)-Equivariant Policy](https://mlsanity.com/paper/2505.18474/) (2025)
- [Safe Learning for Contact-Rich Robot Tasks: A Survey from Classical Learning-Based Methods to Safe Foundation Models](https://mlsanity.com/paper/2512.11908/) (2025)
- [GelFlow: Self-supervised Learning of Optical Flow for Vision-Based Tactile Sensor Displacement Measurement](https://mlsanity.com/paper/2309.06735/) (2023)
- [VibeCheck: Using Active Acoustic Tactile Sensing for Contact-Rich Manipulation](https://mlsanity.com/paper/2504.15535/) (2025)
- [MiVLA: Towards Generalizable Vision-Language-Action Model with Human-Robot Mutual Imitation Pre-training](https://mlsanity.com/paper/2512.15411/) (2025)
- [EquiForm: Noise-Robust SE(3)-Equivariant Policy Learning from 3D Point Clouds](https://mlsanity.com/paper/2601.17486/) (2026)

## Built on by (most cited first, 19 of 19)

- [TacForeSight: Force-Guided Tactile World Model for Contact-Rich Manipulation](https://mlsanity.com/paper/2606.11184/) (2026)
- [FlexiTac: A Low-Cost, Open-Source, Scalable Tactile Sensing Solution for Robotic Systems](https://mlsanity.com/paper/2604.28156/) (2026)
- [DAM-VLA: Decoupled Asynchronous Multimodal Vision Language Action model](https://mlsanity.com/paper/2606.12105/) (2026)
- [UniTacVLA: Unified Tactile Understanding and Prediction in Vision Language Action Models](https://mlsanity.com/paper/2606.31723/) (2026)
- [LIBERO-Occ: Evaluating and Improving Vision-Language-Action Models under Scene-Induced Occlusion via Viewpoint Imagination](https://mlsanity.com/paper/2606.10862/) (2026)
- [TacCoRL: Integrating Tactile Feedback into VLA via Simulation](https://mlsanity.com/paper/2606.11743/) (2026)
- [FM-VLA: Force-based Memory for Vision-Language-Action Models in Contact-Rich Manipulation](https://mlsanity.com/paper/2607.18231/) (2026)
- [Bench2Dex: Benchmarking Visuo-Tactile Bimanual Dexterous Manipulation Across Dexterous Hands](https://mlsanity.com/paper/2609.15726/) (2026)
- [TAP-VLA: Tactile Annotation Prompting for Vision Language Action Models](https://mlsanity.com/paper/2606.29089/) (2026)
- [TACO: TActile World Model as a Self-COrrector for Scalable Robot Policy Post-Training](https://mlsanity.com/paper/2607.02840/) (2026)
- [Learning Physical Interaction: A Survey of Tactile- and Force-aware Robot Learning](https://mlsanity.com/paper/2608.07558/) (2026)
- [Tac2Pix: Image-Space Visuo-Tactile Fusion for Dexterous Manipulation](https://mlsanity.com/paper/2606.08765/) (2026)
- [Sensing Which Modality Matters: Evidence-Gated Regularization for Robust VLA Policies](https://mlsanity.com/paper/2609.03142/) (2026)
- [ForeTac-VLA: A Forecasting-Based Tactile-Vision-Language-Action Model for Contact-Rich Robotic Manipulation](https://mlsanity.com/paper/2609.20980/) (2026)
- [VT-Bridge: Bridging Pretrained Foundation VLAs to VTLAs via Lightweight Residual Adaptation](https://mlsanity.com/paper/2609.22606/) (2026)
- [Tactile-JEPA: Topology-Aware Self-Supervised Representation Learning for Distributed Tactile Sensors](https://mlsanity.com/paper/2609.24385/) (2026)
- [Learning tactile perception from high-bandwidth single-point sensing](https://mlsanity.com/paper/2609.24621/) (2026)
- [TacDyn-WAM: Learning Implicit Tactile Dynamics in a Heterogeneous Visuo-Tactile World Action Model](https://mlsanity.com/paper/2610.00638/) (2026)
- [SPECTRA-VLA: Base-Fine Hyperspectral Token Transfer at Frozen Vision-Language-Action Interfaces](https://mlsanity.com/paper/2610.19651/) (2026)

## Abstract (arXiv)

Vision-Language-Action (VLA) models have demonstrated significant advantages in robotic manipulation. However, their reliance on vision and language often leads to suboptimal performance in tasks involving visual occlusion, fine-grained manipulation, and physical contact. To address these challenges, we propose TacVLA, a fine-tuned VLA model by incorporating tactile modalities into the transformer-based policy to enhance fine-grained manipulation capabilities. Specifically, we introduce a contact-aware gating mechanism that selectively activates tactile tokens only when contact is detected, enabling adaptive multimodal fusion while avoiding irrelevant tactile interference. The fused visual, language, and tactile tokens are jointly processed within the transformer architecture to strengthen cross-modal grounding during contact-rich interaction. Extensive experiments on constraint-locked disassembly, in-box picking and robustness evaluations demonstrate that TacVLA outperforms baselines, %including existing VLA models and diffusion policies, improving the performance by averaging 20% success rate in disassembly and 60% in in-box picking, achieving a 2.1× improvement under visual occlusion, and showing recovery behavior under human disturbance. Videos are available at https://sites.google.com/view/tacvla.