具有约束意识的扩散先验用于高保真和多功能四足运动
Constraint-Aware Diffusion Priors for High-Fidelity and Versatile Quadruped Locomotion
AI总结：
本文提出Diff-CAST框架，利用扩散模型多模态分布建模能力解决传统GAN判别器模式崩溃和运动分布捕捉问题，通过Symmetric Augmented Command Conditioning和Constrained RL实现高保真四足运动控制。
摘要：
强化学习结合模仿学习显著推动了仿生四足运动发展。然而，将这些框架扩展到大规模、多源数据集时暴露了根本性瓶颈。传统基于GAN的判别器易出现模式崩溃，难以捕捉未整理数据集中的多样化运动分布。现有运动先验存在分布外（OOD）跟踪冲突，导致复杂 maneuver 期间严重意外航向漂移。部署无约束先验到物理硬件会因忽视执行器动力学而造成安全风险。为克服这些挑战，我们提出Diff-CAST（扩散引导的约束意识对称跟踪）框架，利用扩散模型多模态分布建模能力实现风格化奖励。Diff-CAST有效替代传统GAN判别器，解锁在异构数据集上的稳健数据扩展。为确保高保真意图执行和可靠现实部署，我们引入综合Sim2Real架构，集成对称增强命令条件（SACC）实现无漂移跟踪，以及约束强化学习确保硬件安全。在四足机器人实验中，Diff-CAST缓解模式崩溃，实现多样化技能无缝过渡，并确保稳健、符合硬件的运动。
英文摘要：
Reinforcement learning combined with imitation learning has significantly advanced biomimetic quadrupedal locomotion. However, scaling these frameworks to massive, multi-source datasets exposes fundamental bottlenecks. First, traditional GAN-based discriminators are prone to mode collapse, struggling to capture diverse motion distributions from uncurated datasets. Second, existing kinematic priors suffer from out-of-distribution (OOD) tracking conflicts, leading to severe unintended heading drifts during complex maneuvers. Furthermore, deploying unconstrained priors to physical hardware poses critical safety risks by disregarding actuator dynamics. To overcome these challenges, we propose Diff-CAST (Diffusion-guided Constraint-Aware Symmetric Tracking), a novel motion prior framework leveraging the multi-modal distribution modeling capabilities of diffusion models for stylistic rewards. Diff-CAST effectively replaces traditional GAN discriminators, unlocking robust data scaling on heterogeneous collections. To ensure high-fidelity intent execution and reliable real-world deployment, we introduce a comprehensive Sim2Real architecture integrating Symmetric Augmented Command Conditioning (SACC) for drift-free tracking, and Constrained RL for hardware safety. Experiments on a quadruped demonstrate that Diff-CAST mitigates mode collapse, enables seamless transitions between diverse skills, and ensures robust, hardware-compliant locomotion.
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。
