| title | MambaAct: Efficient Mamba Action Experts for Flow-Matching Vision-Language-Action Models |
|---|---|
| authors | Khuyen Pham, Tran Nguyen Le, Edoardo Fazzari, Cesare Stefanini |
| date | 2026-10-16 |
| venue | arXiv (preprint, not peer reviewed) |
| id | arXiv:2610.22951 |
| affiliation | Mohamed Bin Zayed University of Artificial Intelligence |
| upvotes | 14 (Hugging Face Daily Papers, 2026-11-01) |
| links | abs pdf hf code |
| primer | Vision-language-action models |
| abstract | Vision-language-action (VLA) models have shown strong promise for generalist robotic manipulation, but their inference cost remains a major bottleneck for real-time control. This limitation is especially pronounced in flow-matching VLAs such as π_{0.5}, where the action expert is evaluated repeatedly during iterative denoising. We introduce MambaAct, an efficient Mamba action expert for flow-matching VLA policies. MambaAct retains the pretrained visual-language prefix encoder of π_{0.5}, while replacing its transformer-based action expert with a compact Mamba-based action expert. Within this expert, Mamba serves as the action mixer that models dependencies across the noisy continuous-action sequence. To preserve visual-language conditioning without repeated token-level cross-attention, MambaAct uses a lightweight Context Compressor to map Vision-language model (VLM) prefix tokens into a compact conditioning signal that gates and modulates each Mamba action block. MambaAct uses a 10times smaller action expert and reduces flow-matching denoising latency by 64.8% on LIBERO and 58.8% in real-world deployment, while improving mean LIBERO success by 1.08 percentage points across three independent fine-tuning seeds and maintaining gains on LIBERO-Plus and real-world benchmarks. Our code and model weights are publicly available at https://github.com/giakhuyendihoc/MambaAct. |
cite
@misc{pham2026mambaact,
title={MambaAct: Efficient Mamba Action Experts for Flow-Matching Vision-Language-Action Models},
author={Khuyen Pham and Tran Nguyen Le and Edoardo Fazzari and Cesare Stefanini},
year={2026},
eprint={2610.22951},
archivePrefix={arXiv},
url={https://arxiv.org/abs/2610.22951}
}