# Demystifying When and Why VLAs Fail in Contact-Rich Tasks and How to Fix Them

arXiv: https://arxiv.org/abs/2608.01402 · HTML: https://mlsanity.com/paper/2608.01402/

## Key facts

- Cited by 2 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 20 of its 26 references that are in the map.
- First posted on arXiv 2 Aug 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, 20 of 20)

- [Flow Matching for Generative Modeling](https://mlsanity.com/paper/2210.02747/) (2022)
- [Scaling Rectified Flow Transformers for High-Resolution Image Synthesis](https://mlsanity.com/paper/2403.03206/) (2024)
- [RT-2: Vision-Language-Action Models Transfer Web Knowledge to Robotic Control](https://mlsanity.com/paper/2307.15818/) (2023)
- [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)
- [π0.5: a Vision-Language-Action Model with Open-World Generalization](https://mlsanity.com/paper/2504.16054/) (2025)
- [An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling](https://mlsanity.com/paper/1803.01271/) (2018)
- [GR00T N1: An Open Foundation Model for Generalist Humanoid Robots](https://mlsanity.com/paper/2503.14734/) (2025)
- [Open X-Embodiment: Robotic Learning Datasets and RT-X Models](https://mlsanity.com/paper/2310.08864/) (2023)
- [SmolVLA: A Vision-Language-Action Model for Affordable and Efficient Robotics](https://mlsanity.com/paper/2506.01844/) (2025)
- [ForceVLA: Enhancing VLA Models with a Force-aware MoE for Contact-rich Manipulation](https://mlsanity.com/paper/2505.22159/) (2025)
- [Making Sense of Vision and Touch: Learning Multimodal Representations for Contact-Rich Tasks](https://mlsanity.com/paper/1907.13098/) (2019)
- [VLA-Touch: Enhancing Vision-Language-Action Models with Dual-Level Tactile Feedback](https://mlsanity.com/paper/2507.17294/) (2025)
- [FACTR: Force-Attending Curriculum Training for Contact-Rich Policy Learning](https://mlsanity.com/paper/2502.17432/) (2025)
- [FoAR: Force-Aware Reactive Policy for Contact-Rich Robotic Manipulation](https://mlsanity.com/paper/2411.15753/) (2024)
- [DexForce: Extracting Force-informed Actions from Kinesthetic Demonstrations for Dexterous Manipulation](https://mlsanity.com/paper/2501.10356/) (2025)
- [FORGE: Force-Guided Exploration for Robust Contact-Rich Manipulation under Uncertainty](https://mlsanity.com/paper/2408.04587/) (2024)
- [FD-VLA: Force-Distilled Vision-Language-Action Model for Contact-Rich Manipulation](https://mlsanity.com/paper/2602.02142/) (2026)
- [FAVLA: A Force-Adaptive Fast-Slow VLA model for Contact-Rich Robotic Manipulation](https://mlsanity.com/paper/2602.23648/) (2026)
- [CRAFT: Adapting VLA Models to Contact-rich Manipulation via Force-aware Curriculum Fine-tuning](https://mlsanity.com/paper/2602.12532/) (2026)

## Built on by (most cited first, 2 of 2)

- [SoTa: Soft Tactile Skins for Dexterous Manipulation](https://mlsanity.com/paper/2610.02338/) (2026)
- [Mulligan: Performance-Guided Data Collection for Efficient On-Robot Learning](https://mlsanity.com/paper/2610.05882/) (2026)

## Abstract (arXiv)

We address the problem of understanding when and why Vision-Language-Action models struggle with contact-rich manipulation tasks that require precise physical interaction. Prior work has primarily focused on addressing contact failures through force-augmented architectures and training-time regularizers, yet the root causes of these failures remain underexplored. We identify two distinct failure modes underlying this gap. Precision failures are rooted in a flow-matching policy training mismatch, and force failures arise from the distinctive structure of force signals. We address each failure mode with a targeted mechanism and combine them into FACT, which achieves 66% average success rate across five contact-rich tasks against 41% for the best prior baseline, in an evaluation spanning almost 2,500 real-world rollouts.