Due to brain-body co-evolution, animals' intrinsic body dynamics play a crucial role in energy-efficient locomotion, which shares control effort between active muscles and passive body dynamics -- a principle known as Embodied Physical Intelligence. In contrast, robot bodies are often designed with one centralised controller that typically suppress the intrinsic body dynamics instead of exploiting it. We introduce Physical Imitation Learning (PIL), which distils a Reinforcement Learning (RL) control policy into physically implementable body responses that can be directly offloaded to passive Parallel Elastic Joints (PEJs), enabling therefore the body to imitate part of the controlled behaviour. Meanwhile, the residual policy commands the motors to recover the RL policy's performance. The results is an overall reduced energy consumption thanks to outsourcing parts of the control policy to the PEJs. Here we show in simulated quadrupeds, that our PIL approach can offloads up to 87% of mechanical power to PEJs on flat terrain and 18% on rough terrain. Because the body design is distilled from -- rather than jointly optimised with -- the control policy, PIL realises brain-body co-design without expanding the search space with body design parameters, providing a computationally efficient route to task-specific Embodied Physical Intelligence applicable to a wide range of joint-based robot morphologies.

翻译：由于脑-体协同进化，动物内在的身体动力学在节能运动中起着关键作用——通过主动肌肉与被动身体动力学分担控制负担，这一原理被称为“具身物理智能”。相比之下，机器人身体通常采用集中式控制器设计，往往抑制而非利用其内在身体动力学。我们提出物理模仿学习（PIL），该方法将强化学习（RL）控制策略蒸馏为可直接卸载至无动并联弹性关节（PEJs）的物理可实现的体响应，从而使身体能够模仿部分受控行为。同时，残差策略驱动电机恢复强化学习策略的性能。由于将部分控制策略外化至PEJs，最终实现了整体能耗降低。我们在仿真四足机器人中证明，PIL方法在平坦地形上可将高达87%的机械功率卸载至PEJs，在崎岖地形中为18%。由于身体设计是通过控制策略蒸馏（而非联合优化）得到，PIL在不扩展身体设计参数的搜索空间情况下实现了脑-体协同设计，为面向任务的具身物理智能提供了一种计算高效的路径，适用于多种基于关节的机器人形态。