统一对象中心世界模型与扩散策略：多阶段机器人任务的分层框架
Unifying Object-Centric World Models and Diffusion Policy: A Hierarchical Framework for Multi-Stage Robotic Tasks
AI总结：
提出WorldDP分层框架，结合高层世界模型进行运行时子目标优化和低层扩散策略执行，利用对象中心表示解耦环境实体，实现多阶段机器人操作任务的有效规划与执行。
摘要：
视觉世界模型在学习复杂系统动力学方面显示出巨大潜力。最近的进展利用这些模型作为模型预测控制（MPC）框架中的转移函数来解决各种控制任务。然而，当应用于机器人时，它们仅限于单阶段任务（如抓取或到达），难以处理需要复杂序列规划的多阶段任务。在这项工作中，我们引入了WorldDP，一个专为多阶段机器人操作设计的世界模型框架。我们的分层方法利用高层世界模型作为转移函数，在运行时优化可行的子目标，随后由低层扩散策略实现这些子目标。为了进一步辅助学习动力学和规划，我们结合了对象中心表示，这些表示解耦了环境实体，并使我们能够针对每个实体进行顺序规划。在多个机器人基准测试中，WorldDP始终优于现有基线，验证了将世界模型的物理基础规划与扩散策略的高效执行相结合，能够产生更优的多阶段性能。
英文摘要：
Visual world models have shown great potential in learning complex system dynamics. Recent advancements leverage these models as transition functions within Model Predictive Control (MPC) frameworks to solve various control tasks. When applied to robotics, however, they are limited to single-stage tasks such as reaching or grasping, and struggle with multi-stage ones that demand complex sequential planning. In this work, we introduce WorldDP, a world model framework designed for multi-stage robotic manipulation. Our hierarchical approach utilizes a high-level world model as a transition function to optimize for feasible subgoals during runtime, which are subsequently reached by a low-level Diffusion Policy. To further aid in learning dynamics and planning, we incorporate object-centric representations that decouple environmental entities and enable us to plan sequentially with respect to each. Evaluated across several robotics benchmarks, WorldDP consistently outperforms existing baselines, validating that coupling the world model's physically grounded planning with diffusion policy's efficient execution yields superior multi-stage performance. Project Page: https://raktimgg.github.io/worlddp-website/.
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。
