ProgressVLA: 進捗ガイド付き拡散ポリシーによる視覚言語ロボット操作
ProgressVLA: Progress-Guided Diffusion Policy for Vision-Language Robotic Manipulation
ロボット操作のための視覚言語行動モデルに、タスク進捗の推定と微分可能な進捗ガイダンスを導入し、長期的なタスクでの成功率と汎化性能を向上させた。
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著者: Hongyu Yan, Qiwei Li, Jiaolong Yang, Yadong Mu
分類: cs.RO, cs.AI
原文アブストラクト
Most existing vision-language-action (VLA) models for robotic manipulation lack progress awareness, typically relying on hand-crafted heuristics for task termination. This limitation is particularly severe in long-horizon tasks involving cascaded sub-goals. In this work, we investigate the estimation and integration of task progress, proposing a novel model named {\textbf \vla}. Our technical contributions are twofold: (1) \emph{robust progress estimation}: We pre-train a progress estimator on large-scale, unsupervised video-text robotic datasets. This estimator achieves a low prediction residual (0.07 on a scale of $[0, 1]$) in simulation and demonstrates zero-shot generalization to unseen real-world samples, and (2) \emph{differentiable progress guidance}: We introduce an inverse dynamics world model that maps predicted action tokens into future latent visual states. These latents are then processed by the progress estimator; by applying a maximal progress regularization, we establish a differentiable pipeline that provides progress-piloted guidance to refine action tokens. Extensive experiments on the CALVIN and LIBERO benchmarks, alongside real-world robot deployment, consistently demonstrate substantial improvements in success rates and generalization over strong baselines.
