发表机构
Mechatronics, Systems and Control Lab (MSC Lab), Department of Mechanical Engineering, Korea Advanced Institute of Science and Technology (KAIST); Samsung Electronics, Future Robotics AI Group; School of Electrical Engineering, Hanyang University; Advanced Robotics Research Center, Korea Institute of Machinery & Materials (KIMM)（韩国科学技术院机械工程系机电一体化、系统与控制实验室（MSC实验室）; 三星电子未来机器人人工智能集团; 汉阳大学电气工程学院; 韩国机械与材料研究所先进机器人研究中心）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
提出对抗动力学先验（ADP）用于类人机器人抗干扰运动控制。以动力学特征取代运动学特征作对抗目标，用轨迹优化建参考数据集训练鉴别器，提升机器人抗干扰及运动跟踪能力。
AI 中文摘要
本文中，我们提出了用于抗干扰类人机器人运动控制的对抗动力学先验（ADP）。现有的基于运动先验的方法通过模仿运动学运动特征来诱导自然运动风格，但它们没有直接对动力学特征进行正则化，如质心运动、质心动量、接触力和接触状态。为了解决这一限制，我们用从运动轨迹中提取的选定动力学特征取代运动学运动风格特征作为对抗目标。为此，我们使用轨迹优化来构建一个参考数据集，并训练一个鉴别器来评估策略诱导的时间窗口是否与所得参考一致。通过显式运动跟踪，ADP鼓励策略展开即使在受到干扰后也保持接近参考支持。实验结果表明，与我们评估中最强的基线AMP相比，ADP将80%成功脉冲阈值（$J_{80}$）提高了16.7%，同时将方向平均恢复时间和速度跟踪误差分别降低了47.9%和35.4%。
英文摘要
In this paper, we propose Adversarial Dynamics Priors (ADP) for perturbation-resilient humanoid locomotion control. Existing motion prior-based methods induce natural motion styles by imitating kinematic motion features, but they do not directly regularize dynamics features, such as CoM motion, centroidal momentum, contact forces, and contact states. To address this limitation, we replace kinematic motion-style feature with selected dynamics features extracted from locomotion trajectories as the target of adversarial regularization. To this end, we use trajectory optimization to construct a reference dataset and train a discriminator to evaluate whether policy-induced temporal windows are consistent with the resulting reference distribution. Without explicit motion tracking, ADP encourages policy rollouts to remain close to the reference support, even after perturbations. Experimental results show that, compared with AMP, the strongest baseline in our evaluation, ADP improves the $80\%$-success impulse threshold ($J_{80}$) by $16.7\%$, while reducing direction-averaged recovery time and velocity tracking error by $47.9\%$ and $35.4\%$, respectively.
Comments8 pages, 6 figures
