DAM-VLA: A Dynamic Action Model-Based Vision-Language-Action Framework for Robot Manipulation
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2026
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866914361423953920 |
|---|---|
| author | Peng, Xiongfeng Yu, Jiaqian Li, Dingzhe Jin, Yixiang Xu, Lu Mao, Yamin Zhang, Chao Li, Weiming Jang, Sujin Lee, Dongwook Ji, Daehyun |
| author_facet | Peng, Xiongfeng Yu, Jiaqian Li, Dingzhe Jin, Yixiang Xu, Lu Mao, Yamin Zhang, Chao Li, Weiming Jang, Sujin Lee, Dongwook Ji, Daehyun |
| contents | In dynamic environments such as warehouses, hospitals, and homes, robots must seamlessly transition between gross motion and precise manipulations to complete complex tasks. However, current Vision-Language-Action (VLA) frameworks, largely adapted from pre-trained Vision-Language Models (VLMs), often struggle to reconcile general task adaptability with the specialized precision required for intricate manipulation. To address this challenge, we propose DAM-VLA, a dynamic action model-based VLA framework. DAM-VLA integrates VLM reasoning with diffusion-based action models specialized for arm and gripper control. Specifically, it introduces (i) an action routing mechanism, using task-specific visual and linguistic cues to select appropriate action models (e.g., arm movement or gripper manipulation), (ii) a dynamic action model that fuses high-level VLM cognition with low-level visual features to predict actions, and (iii) a dual-scale action weighting mechanism that enables dynamic coordination between the arm-movement and gripper-manipulation models. Across extensive evaluations, DAM-VLA achieves superior success rates compared to state-of-the-art VLA methods in simulated (SIMPLER, FurnitureBench) and real-world settings, showing robust generalization from standard pick-and-place to demanding long-horizon and contact-rich tasks. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_00926 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | DAM-VLA: A Dynamic Action Model-Based Vision-Language-Action Framework for Robot Manipulation Peng, Xiongfeng Yu, Jiaqian Li, Dingzhe Jin, Yixiang Xu, Lu Mao, Yamin Zhang, Chao Li, Weiming Jang, Sujin Lee, Dongwook Ji, Daehyun Robotics In dynamic environments such as warehouses, hospitals, and homes, robots must seamlessly transition between gross motion and precise manipulations to complete complex tasks. However, current Vision-Language-Action (VLA) frameworks, largely adapted from pre-trained Vision-Language Models (VLMs), often struggle to reconcile general task adaptability with the specialized precision required for intricate manipulation. To address this challenge, we propose DAM-VLA, a dynamic action model-based VLA framework. DAM-VLA integrates VLM reasoning with diffusion-based action models specialized for arm and gripper control. Specifically, it introduces (i) an action routing mechanism, using task-specific visual and linguistic cues to select appropriate action models (e.g., arm movement or gripper manipulation), (ii) a dynamic action model that fuses high-level VLM cognition with low-level visual features to predict actions, and (iii) a dual-scale action weighting mechanism that enables dynamic coordination between the arm-movement and gripper-manipulation models. Across extensive evaluations, DAM-VLA achieves superior success rates compared to state-of-the-art VLA methods in simulated (SIMPLER, FurnitureBench) and real-world settings, showing robust generalization from standard pick-and-place to demanding long-horizon and contact-rich tasks. |
| title | DAM-VLA: A Dynamic Action Model-Based Vision-Language-Action Framework for Robot Manipulation |
| topic | Robotics |
| url | https://arxiv.org/abs/2603.00926 |