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README.md

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-license: apache-2.0
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+license: mit
+---
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+# **PALM: Progress-Aware Policy Learning via Affordance Reasoning for Long-Horizon Robotic Manipulation**
+![Teaser](main.png)
+______________________________________________________________________
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+## 💡 Introduction
+<div align="center">
+
+<a href="#"><img src="https://img.shields.io/badge/Project-Page-blue?style=flat-square&logo=googlechrome&logoColor=white" alt="Project Page"></a>
+<a href="https://arxiv.org/abs/2601.07060"><img src="https://img.shields.io/badge/Arxiv-2601.07060-red?style=flat-square&logo=arxiv&logoColor=white" alt="Arxiv"></a>
+<a href="https://huggingface.co/PLAN-Lab/PALM"><img src="https://img.shields.io/badge/Hugging%20Face-Model-yellow?style=flat-square&logo=huggingface&logoColor=black" alt="Hugging Face"></a>
+
+</div>
+
+**Yuanzhe Liu, Jingyuan Zhu, Yuchen Mo, Gen Li, Xu Cao, Jin Jin, Yifan Shen, Zhengyuan Li, Tianjiao Yu, Wenzhen Yuan, Fangqiang Ding, Ismini Lourentzou**
+
+Recent advancements in vision-language-action (VLA) models have shown promise in robotic manipulation, yet they continue to struggle with long-horizon, multi-step tasks. Existing methods lack internal reasoning mechanisms that can identify task-relevant interaction cues or track progress within a subtask, leading to critical execution errors such as repeated actions, missed steps, and premature termination. To address these challenges, we introduce PALM, a VLA framework that structures policy learning around interaction-centric affordance reasoning and subtask progress cues. PALM distills complementary affordance representations that capture object relevance, contact geometry, spatial placements, and motion dynamics, and serve as task-relevant anchors for visuomotor control. To further stabilize long-horizon execution, PALM predicts continuous within-subtask progress, enabling seamless subtask transitions. Across extensive simulation and real-world experiments, PALM consistently outperforms baselines, achieving a $91.8 \%$ success rate on LIBERO-LONG, a $12.5 \%$ improvement in average length on CALVIN ABC → D , and a $2 \times$ improvement over real-world baselines across three long-horizon generalization settings.