让四足机器人学会理解自身动力学，跑得更稳更省力。
Dynamics Aware Quadrupedal Locomotion via Intrinsic Dynamics Head
- 训练时加入动力学预测头，让控制策略理解身体运动规律。
- 仿真中提升效率和流畅度，真实机器人扭矩效率提高16.8%。
- 适合关注机器人稳定性与能效优化的研究者与工程师。
四足运动在复杂地形中实现敏捷、多样的移动至关重要。理解并估计底层物理动力学是实现高效稳定四足运动的关键。我们提出一种新型训练框架，使控制策略能够理解和推理物理动力学。在仿真中，我们同时训练一个内在动力学（ID）头，学习状态到力矩的动力学关系，并定义由ID头驱动的动力学奖励，促使策略趋向更可预测的动力学行为。我们还提供一种通过调节ID头的训练系数来调整策略中学习到的动力学的机制。仿真实验表明，该机制在多种标准四足运动奖励下均能引导收敛至更优解，产生更高效、更平滑的策略。真实机器人实验验证了这些改进的仿真到现实迁移效果，扭矩效率提升16.8%，动作频率提高18.6%，机械功率降低12.8%，安全扭矩占用率提升6.4%。
原文摘要 · Abstract (English)
Quadrupedal locomotion plays a critical role in enabling agile, versatile movement across complex terrains. Understanding and estimating the underlying physical dynamics are essential for achieving efficient and stable quadrupedal locomotion. We propose a novel training framework for quadrupedal locomotion that enables the Control Policy to understand and reason about physical dynamics. In simulation, we concurrently train an Intrinsic Dynamics (ID) Head that learns state-to-torque dynamics alongside the Control Policy, and we define a dynamics reward enabled by the ID Head that encourages the Policy toward more predictable dynamical behavior. We also provide a mechanism to tune the learned dynamics in the resulting Policy by controlling the training coefficients of the ID Head. Our simulation experiments show that this mechanism drives convergence to better optima across a wide range of standard quadrupedal locomotion rewards, yielding more efficient and smoother policies. Our real-robot experiments demonstrate sim-to-real transfer of these improvements, with significant gains in torque efficiency (16.8%), action rate (18.6%), and mechanical power (12.8%), while improving safe torque occupancy by 6.4%.
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