DreamPolicy: 一种统一的世界模型策略用于可扩展的人形机器人运动
DreamPolicy: A Unified World-model Policy for Scalable Humanoid Locomotion
AI总结：
DreamPolicy通过整合离线数据与扩散式世界模型，实现单一策略掌握已知和未知地形。其地形感知世界模型能生成物理合理的未来轨迹，作为动态目标指导策略，避免手动奖励工程。实验表明，DreamPolicy在未知和复合地形上性能优于基线38%。
摘要：
实现单一策略的多样化人形运动存在关键可扩展性挑战。现有方法常依赖于将多个地形特定的教师策略蒸馏为统一的学生策略。然而，此类蒸馏仅能捕捉基本运动原语，难以有机组合这些技能以适应复杂环境，导致对新复合地形的泛化能力差。为克服这一问题，我们提出了DreamPolicy，一种整合离线数据与基于扩散的世界模型的统一框架，使单一策略能掌握已知和未知地形。核心是地形感知的世界模型，由基于聚合轨迹的自回归扩散世界模型驱动。该模型生成物理上合理的未来轨迹，作为动态目标指导策略，从而避免手动奖励工程。与蒸馏不同，我们的世界模型捕捉到可泛化运动技能，使策略能稳健地转移到未知复合地形。DreamPolicy能自然扩展到数据可用性。随着离线数据集扩大，扩散世界模型持续获得更丰富的技能。实验表明，DreamPolicy在未知地形上性能优于基线高达27%，在复合地形上达38%。通过统一基于世界模型的规划和策略学习，DreamPolicy突破了“一任务一策略”的瓶颈，建立了可扩展、数据驱动的通用人形控制范式。
英文摘要：
Achieving versatile humanoid locomotion with a single policy presents a critical scalability challenge. Prevailing methods often rely on distilling multiple terrain-specific teacher policies into a unified student policy. However, while such distillation captures basic locomotion primitives, it struggles to organically compose these skills to adapt to complex environments, resulting in poor generalization to novel composite terrains unseen during training. To overcome this, we present DreamPolicy, a unified framework that integrates offline data with a diffusion-based world model, enabling a single policy to master both known and unseen terrains. Central to our approach is a terrain-aware world model, driven by an autoregressive diffusion world model trained on aggregated rollouts from specialized policies. This model synthesizes physically plausible future trajectories, which serve as dynamic objectives for a conditioned policy, thereby bypassing manual reward engineering. Unlike distillation, our world model captures generalizable locomotion skills, allowing for robust zero-shot transfer to unseen composite terrains. DreamPolicy naturally scales with data availability. As the offline dataset expands, the diffusion world model continuously acquires richer skills. Experiments demonstrate that DreamPolicy outperforms the strongest baseline by up to 27\% on unseen terrains and 38\% on combined terrains. By unifying world model-based planning and policy learning, DreamPolicy breaks the "one task, one policy" bottleneck and establishes a scalable, data-driven paradigm for generalist humanoid control.
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。
