物理模仿学习：将控制策略蒸馏到被动弹性中
Physical Imitation Learning: Distilling Control Policies into Passive Elasticity
AI总结：
提出物理模仿学习(PIL)方法，将强化学习控制策略分解为主动与被动部分，被动部分卸载到并联弹性关节，显著降低能耗，在模拟四足机器人上实现高达95%的机械功率卸载。
摘要：
由于脑-体协同进化，动物的内在身体动力学在其节能运动中起着关键作用。具体来说，控制努力在主动肌肉和被动身体动力学之间共享——这一原则通常被称为物理智能。因此，身体动力学是解决方案的一部分。相比之下，机器人身体通常被设计得尽可能简单，但主动控制常常与内在身体动力学对抗，导致低能效。我们引入了物理模仿学习（PIL），这是一种新颖的方法，使当前的机器人控制更接近动物。PIL 获取通过强化学习（RL）获得的学习控制策略，并将其系统地分解为主动和被动控制贡献。然后，被动部分可以直接卸载到被动并联弹性关节（PEJ）上。结果，主动控制贡献显著减少，降低了整体能耗。此外，策略可以通过 RL 训练，通过生成更容易被 PEJ 模仿的步态来利用 PEJ 的辅助。这使得主动和被动控制组件的协同设计成为可能，将更大份额的驱动努力转移到 PEJ。在这里，我们在模拟四足动物中展示了这种方法的潜力。我们的结果表明，所提出的方法可以在平坦地形上卸载高达 95% 的机械功率到被动身体动力学，在崎岖地形上卸载 13%。因此，PIL 提供了一条可推广的途径，用于实现特定任务的物理智能，适用于各种基于关节的机器人形态。
英文摘要：
Due to brain-body co-evolution, animals' intrinsic body dynamics play a crucial role in their energy-efficient locomotion. Specifically, the control effort is shared between active muscles and passive body dynamics--a principle often referred to as Physical Intelligence. As a result, the body dynamics are part of the solution. In contrast, robot bodies are typically designed to be as simple as possible, but the active control often fights the intrinsic body dynamics, resulting in low energy-efficiency. We introduce Physical Imitation Learning (PIL), a novel approach that brings current robotics control closer to animals. PIL takes learned control policies obtained with Reinforcement Learning (RL) and systematically splits them up into an active and passive control contribution. The passive part can be then directly offloaded to passive Parallel Elastic Joints (PEJs). As a result, the active control contribution is significantly reduced, lowering the overall energy consumption. Furthermore, the policy can be trained via RL to leverage the PEJ assistance by generating gaits that are more readily emulated by the PEJs. This enables co-design of the active and passive control components, shifting a greater share of actuation effort to the PEJs. Here we demonstrate the potential of this approach in simulated quadrupeds. Our results show that the proposed approach can offload up to 95% of mechanical power to passive body dynamics on flat terrain and 13% on rough terrain. PIL thereby provides a generalisable route to task-specific Physical Intelligence applicable to a wide range of joint-based robot morphologies.
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。
