[Paper] DAM-VLA: Decoupled Asynchronous Multimodal Vision Language Action model
Source: arXiv - 2606.12105v1
Overview
Vision-language-action (VLA) models inherit a shared synchronous clock from vision-language pretraining, processing every input at one rate. This is misaligned with physical interaction, where a high-frequency modality changes at hundreds of hertz, vision evolves more slowly, and language stays constant across an episode. A synchronous VLA oversamples slow modalities, undersamples fast ones, and caps action generation at the lowest effective frequency. We hypothesize that decoupling temporal processing per modality, letting each update and retain information at its own sensor rate, yields stronger representations and more robust control. We present DAM-VLA, which maintains per-modality latent buffers refreshed at sensor rates and read continuously by the action head, integrating new high-frequency modalities through gated cross-attention that leaves the pretrained backbone intact. Across seven contact-rich real-world manipulation tasks, DAM-VLA more than doubles the average success rate of the strongest synchronous baseline (95.2% vs.\ 40.95%) while sustaining smooth, reactive 100,Hz control. Project website: \href{https://intuitive-robots.github.io/DAM-VLA/}{intuitive-robots.github.io/DAM-VLA/}
Key Contributions
This paper presents research in the following areas:
- cs.RO
- cs.CV
- cs.LG
Methodology
Please refer to the full paper for detailed methodology.
Practical Implications
This research contributes to the advancement of cs.RO.
Authors
- Pankhuri Vanjani
- Zhuoyue Li
- Jakub Suliga
- Moritz Reuss
- Gianluca Geraci
- Xinkai Jiang
- Rudolf Lioutikov
Paper Information
- arXiv ID: 2606.12105v1
- Categories: cs.RO, cs.CV, cs.LG
- Published: June 10, 2026
- PDF: Download PDF