DAM-VLA: 動的行動モデルに基づくロボット操作のための視覚言語行動フレームワーク
DAM-VLA: A Dynamic Action Model-Based Vision-Language-Action Framework for Robot Manipulation
VLMの推論と拡散ベースの行動モデルを統合し、タスクに応じて腕の移動とグリッパー操作を動的に切り替えるVLAフレームワークを提案。シミュレーションと実環境で高い成功率を達成した。
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著者: Xiongfeng Peng, Jiaqian Yu, Dingzhe Li, Yixiang Jin, Lu Xu, Yamin Mao, Chao Zhang, Weiming Li, Sujin Jang, Dongwook Lee, Daehyun Ji
分類: cs.RO
原文アブストラクト
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.
関連論文
- AnySlot: ゼロショットのスロット配置のための目標条件付き視覚言語行動ポリシーVLA/マニピュレーション
- 概念ゲート型視覚蒸留による視覚言語行動モデルの雑然環境耐性の向上VLA/マニピュレーション
