PHR-VLA: Planning Horizon Reasoning for Vision-Language-Action Models

2026-08-27

AI Summary

本文提出PHR-VLA框架，通过引入未来动态的潜在表示来解决VLAs模型中缺乏对未来任务动态进行推理的问题，从而提高了机器人在精细操作任务中的成功率。

Abstract

Vision-language-action models (VLAs) have shown strong promise for general-purpose robotic manipulation by mapping language instructions and vision observations directly to actions. However, most VLAs primarily condition action prediction on current observations and lack an explicit mechanism for reasoning over future task dynamics, which is particularly important for fine-grained, contact-rich manipulation. We present PHR-VLA, a framework that enables planning-horizon reasoning in VLAs through privileged latent representations of future dynamics. PHR-VLA introduces a lightweight auxiliary future head that, during training, aligns the VLA's internal representations with latent dynamics extracted from future observations. Evaluation results demonstrate that local, contact-centric, patch-level latent dynamics supervision from the wrist camera improves success rate on LIBERO from 84.1% to 88.4% and on real-world disassembly tasks from 63.3% to 82.5%. Patch-level supervision from a third-person camera also improves performance on Meta-World from 56.70% to 57.8%. These results demonstrate that privileged latent dynamics alignment provides an effective training signal for improving anticipatory reasoning in VLA policies. Project website: \href{https://davoodsz.github.io/PHR-VLA.github.io/}{https://davoodsz.github.io/PHR-VLA.github.io/}

Problem

• Vision-language-action models
• future task dynamics
• fine-grained manipulation

Innovation

• Planning Horizon Reasoning
• Privileged Latent Representations
• Future Dynamics
• Contact-Rich Manipulation
• Patch-Level Supervision