DAM-VLA: A Dynamic Action Model-Based Vision-Language-Action Framework for Robot Manipulation

• URL: http://arxiv.org/abs/2603.00926v1
• Date: Sun, 01 Mar 2026 05:16:04 GMT
• Title: DAM-VLA: A Dynamic Action Model-Based Vision-Language-Action Framework for Robot Manipulation
• Abstract summary: DAM-VLA is a dynamic action model-based Vision-Language-Action framework.<n>It integrates VLM reasoning with diffusion-based action models for arm and gripper control.<n> DAM-VLA achieves superior success rates compared to state-of-the-art VLA methods in simulated and real-world settings.
• Score: 21.418782746588263
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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