[2603.27670] ProgressVLA: Progress-Guided Diffusion Policy for Vision-Language Robotic Manipulation

Computer Science > Robotics arXiv:2603.27670 (cs) [Submitted on 29 Mar 2026] Title:ProgressVLA: Progress-Guided Diffusion Policy for Vision-Language Robotic Manipulation Authors:Hongyu Yan, Qiwei Li, Jiaolong Yang, Yadong Mu View a PDF of the paper titled ProgressVLA: Progress-Guided Diffusion Policy for Vision-Language Robotic Manipulation, by Hongyu Yan and 3 other authors View PDF HTML (experimental) Abstract: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 act...