# HapticVLA: Contact-Rich Manipulation via Vision-Language-Action Model without Inference-Time Tactile Sensing

arXiv: https://arxiv.org/abs/2603.15257 · HTML: https://mlsanity.com/paper/2603.15257/

## Key facts

- Cited by 10 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 31 of its 38 references that are in the map.
- First posted on arXiv 16 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, 30 of 31)

- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [OpenVLA: An Open-Source Vision-Language-Action Model](https://mlsanity.com/paper/2406.09246/) (2024)
- [π0: A Vision-Language-Action Flow Model for General Robot Control](https://mlsanity.com/paper/2410.24164/) (2024)
- [LIBERO: Benchmarking Knowledge Transfer for Lifelong Robot Learning](https://mlsanity.com/paper/2306.03310/) (2023)
- [Meta-World: A Benchmark and Evaluation for Multi-Task and Meta Reinforcement Learning](https://mlsanity.com/paper/1910.10897/) (2019)
- [Flow-GRPO: Training Flow Matching Models via Online RL](https://mlsanity.com/paper/2505.05470/) (2025)
- [CALVIN: A Benchmark for Language-Conditioned Policy Learning for Long-Horizon Robot Manipulation Tasks](https://mlsanity.com/paper/2112.03227/) (2021)
- [SmolVLA: A Vision-Language-Action Model for Affordable and Efficient Robotics](https://mlsanity.com/paper/2506.01844/) (2025)
- [X-VLA: Soft-Prompted Transformer as Scalable Cross-Embodiment Vision-Language-Action Model](https://mlsanity.com/paper/2510.10274/) (2025)
- [RoboBrain: A Unified Brain Model for Robotic Manipulation from Abstract to Concrete](https://mlsanity.com/paper/2502.21257/) (2025)
- [ReinFlow: Fine-tuning Flow Matching Policy with Online Reinforcement Learning](https://mlsanity.com/paper/2505.22094/) (2025)
- [ForceVLA: Enhancing VLA Models with a Force-aware MoE for Contact-rich Manipulation](https://mlsanity.com/paper/2505.22159/) (2025)
- [VTLA: Vision-Tactile-Language-Action Model with Preference Learning for Insertion Manipulation](https://mlsanity.com/paper/2505.09577/) (2025)
- [VLA-0: Building State-of-the-Art VLAs with Zero Modification](https://mlsanity.com/paper/2510.13054/) (2025)
- [Online Reward-Weighted Fine-Tuning of Flow Matching with Wasserstein Regularization](https://mlsanity.com/paper/2502.06061/) (2025)
- [Beyond Sight: Finetuning Generalist Robot Policies with Heterogeneous Sensors via Language Grounding](https://mlsanity.com/paper/2501.04693/) (2025)
- [FlowRL: Matching Reward Distributions for LLM Reasoning](https://mlsanity.com/paper/2509.15207/) (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)
- [MoVE-KD: Knowledge Distillation for VLMs with Mixture of Visual Encoders](https://mlsanity.com/paper/2501.01709/) (2025)
- [MLA: A Multisensory Language-Action Model for Multimodal Understanding and Forecasting in Robotic Manipulation](https://mlsanity.com/paper/2509.26642/) (2025)
- [FedVLA: Federated Vision-Language-Action Learning with Dual Gating Mixture-of-Experts for Robotic Manipulation](https://mlsanity.com/paper/2508.02190/) (2025)
- [FD-VLA: Force-Distilled Vision-Language-Action Model for Contact-Rich Manipulation](https://mlsanity.com/paper/2602.02142/) (2026)
- [Reinforcement Learning for Flow-Matching Policies](https://mlsanity.com/paper/2507.15073/) (2025)
- [Balancing Signal and Variance: Adaptive Offline RL Post-Training for VLA Flow Models](https://mlsanity.com/paper/2509.04063/) (2025)
- [FlowRAM: Grounding Flow Matching Policy with Region-Aware Mamba Framework for Robotic Manipulation](https://mlsanity.com/paper/2506.16201/) (2025)
- [TaF-VLA: Tactile-Force Alignment in Vision-Language-Action Models for Force-aware Manipulation](https://mlsanity.com/paper/2601.20321/) (2026)
- [DySL-VLA: Efficient Vision-Language-Action Model Inference via Dynamic-Static Layer-Skipping for Robot Manipulation](https://mlsanity.com/paper/2602.22896/) (2026)
- [ActDistill: General Action-Guided Self-Derived Distillation for Efficient Vision-Language-Action Models](https://mlsanity.com/paper/2511.18082/) (2025)
- [Low Fidelity Visuo-Tactile Pretraining Improves Vision-Only Manipulation Performance](https://mlsanity.com/paper/2406.15639/) (2024)

## Built on by (most cited first, 10 of 10)

- [Human-Centric Transferable Tactile Pre-Training for Dexterous Robotic Manipulation](https://mlsanity.com/paper/2607.01067/) (2026)
- [TORL-VLA: Tactile Guided Online Reinforcement Learning for Contact-Rich Manipulation](https://mlsanity.com/paper/2606.09337/) (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)
- [PhaseLoRA: Control-Regime-Conditioned Low-Rank Adaptation for Continuous-Action Vision-Language-Action Policies](https://mlsanity.com/paper/2608.15285/) (2026)
- [SpaceVLA: Spatially Grounded VLA for Robotic Manipulation with User-Authored Grasp and Place Anchors](https://mlsanity.com/paper/2608.05730/) (2026)
- [VT-Bridge: Bridging Pretrained Foundation VLAs to VTLAs via Lightweight Residual Adaptation](https://mlsanity.com/paper/2609.22606/) (2026)
- [HapticWAM: Distilling Imagined Touch into a World-Action Model without Inference-Time Tactile Sensing](https://mlsanity.com/paper/2609.23888/) (2026)
- [Uni-VLaT: Whole-Body Tactile Adaptation of VLA Policies for Humanoid Loco-Manipulation](https://mlsanity.com/paper/2609.35450/) (2026)
- [SPECTRA-VLA: Base-Fine Hyperspectral Token Transfer at Frozen Vision-Language-Action Interfaces](https://mlsanity.com/paper/2610.19651/) (2026)

## Abstract (arXiv)

Tactile sensing is a crucial capability for Vision-Language-Action (VLA) architectures, as it enables dexterous and safe manipulation in contact-rich tasks. However, reliance on dedicated tactile hardware increases cost and reduces reproducibility across robotic platforms. We argue that tactile-aware manipulation can be learned offline and deployed without direct haptic feedback at inference. To this end, we present HapticVLA, which proceeds in two tightly coupled stages: Safety-Aware Reward-Weighted Flow Matching (SA-RWFM) and Tactile Distillation (TD). SA-RWFM trains a flow-matching action expert that incorporates precomputed, safety-aware tactile rewards penalizing excessive grasping force and suboptimal grasping trajectories. TD further transfers this tactile-aware capability into a conventional VLA: we distill a compact tactile token from the SA-RWFM teacher and train a student VLA to predict that token from vision and state modalities, enabling tactile-aware action generation at inference without requiring on-board tactile sensors. This design preserves contact-rich tactile-aware reasoning within VLA while removing the need for on-board tactile sensors during deployment. On real-world experiments, HapticVLA achieves a mean success rate of 86.7%, consistently outperforming baseline VLAs - including versions provided with direct tactile feedback during inference.