# Physical Imitation Learning: Distilling Control Policies into Passive Elasticity

arXiv: https://arxiv.org/abs/2604.00611 · HTML: https://mlsanity.com/paper/2604.00611/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Real-world robot offline RL, one of 13 directions in Offline RL and imitation learning from data.
- Builds on 8 of its 39 references that are in the map.
- First posted on arXiv 1 Apr 2026.

## Placed in

- Offline RL and imitation learning from data / [Real-world robot offline RL](https://mlsanity.com/offlinerl/real-world-robot-offline-rl/)

## Builds on (most cited first, 8 of 8)

- [Learning to Walk in Minutes Using Massively Parallel Deep Reinforcement Learning](https://mlsanity.com/paper/2109.11978/) (2021)
- [Orbit: A Unified Simulation Framework for Interactive Robot Learning Environments](https://mlsanity.com/paper/2301.04195/) (2023)
- [Scalable Co-Optimization of Morphology and Control in Embodied Machines](https://mlsanity.com/paper/1706.06133/) (2017)
- [Data-efficient Co-Adaptation of Morphology and Behaviour with Deep Reinforcement Learning](https://mlsanity.com/paper/1911.06832/) (2019)
- [Learning-based Design and Control for Quadrupedal Robots with Parallel-Elastic Actuators](https://mlsanity.com/paper/2301.03509/) (2023)
- [ECoDe: A Sample-Efficient Method for Co-Design of Robotic Agents](https://mlsanity.com/paper/2309.04085/) (2023)
- [An Investigation of the Factors Influencing Evolutionary Dynamics in the Joint Evolution of Robot Body and Control](https://mlsanity.com/paper/2403.10303/) (2024)
- [Bird-inspired tendon coupling improves paddling efficiency by shortening phase transition times](https://mlsanity.com/paper/2409.14707/) (2024)

## Abstract (arXiv)

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.