PALM：通过具身理由推理进行长时间 horizon 肢体操作的进展感知策略学习
PALM: Progress-Aware Policy Learning via Affordance Reasoning for Long-Horizon Robotic Manipulation
AI总结：
PALM通过具身理由推理和子任务进展线索，改进长horizon机器人操作的策略学习，实现91.8%的成功率和更长的任务长度。
摘要：
最近在视觉-语言-动作（VLA）模型方面的进展在机器人操作中显示出潜力，但仍然在长horizon、多步骤任务上遇到困难。现有方法缺乏内部推理机制，无法识别任务相关交互线索或跟踪子任务中的进展，导致关键执行错误，如重复动作、遗漏步骤和提前终止。为了解决这些挑战，我们引入了PALM，一个VLA框架，围绕交互中心的具身理由推理和子任务进展线索结构化策略学习。PALM提炼互补的具身表示，捕捉物体相关性、接触几何、空间位置和运动动力学，并作为任务相关的锚点用于视觉-运动控制。为进一步稳定长horizon执行，PALM预测连续的子任务进展，实现无缝的子任务转换。在广泛的模拟和现实世界实验中，PALM一致优于基线，实现了在LIBERO-LONG上的91.8%成功率，在CALVIN ABC->D上的平均长度提高了12.5%，并在三个长horizon泛化设置中实现了两倍于现实世界基线的改进。
英文摘要：
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 2x improvement over real-world baselines across three long-horizon generalization settings.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
