AdaWorldPolicy：基于世界模型的扩散策略与在线自适应学习的机器人操控
AdaWorldPolicy: World-Model-Driven Diffusion Policy with Online Adaptive Learning for Robotic Manipulation
AI总结：
AdaWorldPolicy通过结合世界模型和在线自适应学习，实现机器人操控的高效动态适应。
摘要：
有效的机器人操控需要能够预测物理结果并适应现实环境的策略。在本工作中，我们介绍了一个统一框架，即具有在线自适应学习的基于世界模型的扩散策略（AdaWorldPolicy），以在动态条件下最小化人工干预来增强机器人操控。我们的核心见解是世界模型提供了强大的监督信号，使在动态环境中能够实现在线自适应学习，这可以通过力矩反馈来缓解动态力变化。我们的AdaWorldPolicy集成了一个世界模型、一个动作专家和一个力预测器，全部都作为互连的流匹配扩散变压器（DiT）实现。它们通过多模态自注意力层互连，使在联合学习中能够进行深入的特征交换，同时保持其各自模块化的特点。我们进一步提出了一种新的在线自适应学习（AdaOL）策略，该策略动态地在动作生成模式和未来想象模式之间切换，以驱动所有三个模块的反应更新。这创造了一个强大的闭环机制，能够适应视觉和物理领域变化，同时具有最小的开销。在一系列模拟和真实机器人基准测试中，我们的AdaWorldPolicy实现了最先进的性能，具有动态适应能力，以应对超出分布的情况。
英文摘要：
Effective robotic manipulation requires policies that can anticipate physical outcomes and adapt to real-world environments. Effective robotic manipulation requires policies that can anticipate physical outcomes and adapt to real-world environments. In this work, we introduce a unified framework, World-Model-Driven Diffusion Policy with Online Adaptive Learning (AdaWorldPolicy) to enhance robotic manipulation under dynamic conditions with minimal human involvement. Our core insight is that world models provide strong supervision signals, enabling online adaptive learning in dynamic environments, which can be complemented by force-torque feedback to mitigate dynamic force shifts. Our AdaWorldPolicy integrates a world model, an action expert, and a force predictor-all implemented as interconnected Flow Matching Diffusion Transformers (DiT). They are interconnected via the multi-modal self-attention layers, enabling deep feature exchange for joint learning while preserving their distinct modularity characteristics. We further propose a novel Online Adaptive Learning (AdaOL) strategy that dynamically switches between an Action Generation mode and a Future Imagination mode to drive reactive updates across all three modules. This creates a powerful closed-loop mechanism that adapts to both visual and physical domain shifts with minimal overhead. Across a suite of simulated and real-robot benchmarks, our AdaWorldPolicy achieves state-of-the-art performance, with dynamical adaptive capacity to out-of-distribution scenarios.
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。
