EmbodieDreamer：通过具身世界建模推进策略训练的Real2Sim2Real迁移
EmbodieDreamer: Advancing Real2Sim2Real Transfer for Policy Training via Embodied World Modeling
AI总结：
针对Real2Sim2Real迁移中的物理动力学与视觉外观差距问题，提出EmbodieDreamer框架，通过PhysAligner优化物理参数、VisAligner实现视觉迁移，有效缩小差距并提升策略训练效果。
摘要：
具身人工智能的快速发展导致对大规模、高质量真实世界数据的需求日益增长。然而，收集此类具身数据仍然成本高昂且效率低下。因此，仿真环境已成为训练机器人策略的关键替代方案。但显著的Real2Sim2Real差距仍是关键瓶颈，尤其在物理动力学和视觉外观方面。为应对这一挑战，我们提出EmbodieDreamer，一个从物理和外观双角度缩小Real2Sim2Real差距的新型框架。具体而言，我们提出PhysAligner，一个可微分物理模块，旨在缩小真实与仿真的物理差距。它联合优化机器人特定参数（如控制增益和摩擦系数），使仿真动力学与真实世界观测更好对齐。此外，我们引入VisAligner，其整合条件视频扩散模型，通过将低保真仿真渲染转换为以仿真状态为条件的真实感视频，实现高保真视觉迁移。大量实验验证了EmbodieDreamer的有效性：所提PhysAligner相比模拟退火方法将物理参数估计误差降低3.74%，同时优化速度提升89.91%；在生成的真实感环境中训练机器人策略，经强化学习后真实世界任务的平均任务成功率提升29.17%。代码、模型和数据将公开。
英文摘要：
The rapid advancement of Embodied AI has led to an increasing demand for large-scale, high-quality real-world data. However, collecting such embodied data remains costly and inefficient. As a result, simulation environments have become a crucial surrogate for training robot policies. Yet, the significant Real2Sim2Real gap remains a critical bottleneck, particularly in terms of physical dynamics and visual appearance. To address this challenge, we propose EmbodieDreamer, a novel framework that reduces the Real2Sim2Real gap from both the physics and appearance perspectives. Specifically, we propose PhysAligner, a differentiable physics module designed to reduce the Real2Sim physical gap. It jointly optimizes robot-specific parameters such as control gains and friction coefficients to better align simulated dynamics with real-world observations. In addition, we introduce VisAligner, which incorporates a conditional video diffusion model to bridge the Sim2Real appearance gap by translating low-fidelity simulated renderings into photorealistic videos conditioned on simulation states, enabling high-fidelity visual transfer. Extensive experiments validate the effectiveness of EmbodieDreamer. The proposed PhysAligner reduces physical parameter estimation error by 3.74% compared to simulated annealing methods while improving optimization speed by 89.91\%. Moreover, training robot policies in the generated photorealistic environment leads to a 29.17% improvement in the average task success rate across real-world tasks after reinforcement learning. Code, model and data will be publicly available.
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。
