DiffuseLoco：基于离线数据集的扩散模型实时腿式运动控制
DiffuseLoco: Real-Time Legged Locomotion Control with Diffusion from Offline Datasets
AI总结：
DiffuseLoco提出利用扩散模型从离线多模态数据集训练多技能腿式运动策略，通过滚动时域控制和延迟输入实现实时控制、零样本迁移与边缘部署，在真实四足机器人上优于强化学习和行为克隆基线。
摘要：
本文介绍了DiffuseLoco，一个从离线数据集训练多技能扩散策略的框架，用于动态腿式运动，能够在真实世界的机器人上实时控制多种技能。大规模离线学习已经在计算机视觉、自然语言处理和机器人操作领域取得了突破。然而，扩大腿式机器人运动学习的规模，尤其是在单一策略中包含多种技能，对先前的在线强化学习方法提出了重大挑战。为了应对这一挑战，我们提出了一个新颖的、可扩展的框架，利用扩散模型直接从具有多样化运动技能集的离线多模态数据集中学习。通过针对动态系统实时控制量身定制的设计选择，包括滚动时域控制和延迟输入，DiffuseLoco能够重现执行各种运动技能时的多模态性，零样本迁移到真实四足机器人，并且可以部署在边缘计算设备上。此外，DiffuseLoco展示了技能之间的自由过渡和对环境变化的鲁棒性。通过在真实世界实验中的广泛基准测试，与先前的强化学习和非扩散行为克隆基线相比，DiffuseLoco表现出更好的稳定性和速度跟踪性能。这些设计选择通过全面的消融研究得到了验证。这项工作通过扩展大型表达模型和多样化离线数据集，为扩大基于学习的腿式运动控制器的规模开辟了新的可能性。
英文摘要：
This work introduces DiffuseLoco, a framework for training multi-skill diffusion-based policies for dynamic legged locomotion from offline datasets, enabling real-time control of diverse skills on robots in the real world. Offline learning at scale has led to breakthroughs in computer vision, natural language processing, and robotic manipulation domains. However, scaling up learning for legged robot locomotion, especially with multiple skills in a single policy, presents significant challenges for prior online reinforcement learning methods. To address this challenge, we propose a novel, scalable framework that leverages diffusion models to directly learn from offline multimodal datasets with a diverse set of locomotion skills. With design choices tailored for real-time control in dynamical systems, including receding horizon control and delayed inputs, DiffuseLoco is capable of reproducing multimodality in performing various locomotion skills, zero-shot transfer to real quadrupedal robots, and it can be deployed on edge computing devices. Furthermore, DiffuseLoco demonstrates free transitions between skills and robustness against environmental variations. Through extensive benchmarking in real-world experiments, DiffuseLoco exhibits better stability and velocity tracking performance compared to prior reinforcement learning and non-diffusion-based behavior cloning baselines. The design choices are validated via comprehensive ablation studies. This work opens new possibilities for scaling up learning-based legged locomotion controllers through the scaling of large, expressive models and diverse offline datasets.
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。
