# Learning Speed-Adaptive Walking Agent Using Imitation Learning with Physics-Informed Simulation

arXiv: https://arxiv.org/abs/2412.03949 · HTML: https://mlsanity.com/paper/2412.03949/

## Key facts

- Cited by 5 papers in the map (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 9 of its 38 references that are in the map.
- First posted on arXiv 5 Dec 2024.

## 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, 9 of 9)

- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor](https://mlsanity.com/paper/1801.01290/) (2018)
- [Trust Region Policy Optimization](https://mlsanity.com/paper/1502.05477/) (2015)
- [Unsupervised Deep Embedding for Clustering Analysis](https://mlsanity.com/paper/1511.06335/) (2015)
- [DeepMimic: Example-Guided Deep Reinforcement Learning of Physics-Based Character Skills](https://mlsanity.com/paper/1804.02717/) (2018)
- [Variational Discriminator Bottleneck: Improving Imitation Learning, Inverse RL, and GANs by Constraining Information Flow](https://mlsanity.com/paper/1810.00821/) (2018)
- [LocoMuJoCo: A Comprehensive Imitation Learning Benchmark for Locomotion](https://mlsanity.com/paper/2311.02496/) (2023)
- [Natural and Robust Walking using Reinforcement Learning without Demonstrations in High-Dimensional Musculoskeletal Models](https://mlsanity.com/paper/2309.02976/) (2023)
- [Learning a Control Policy for Fall Prevention on an Assistive Walking Device](https://mlsanity.com/paper/1909.10488/) (2019)

## Built on by (most cited first, 5 of 5)

- [Learning Hip Exoskeleton Control Policy via Predictive Neuromusculoskeletal Simulation](https://mlsanity.com/paper/2603.04166/) (2026)
- [Muscle Synergy Priors Enhance Biomechanical Fidelity in Predictive Musculoskeletal Locomotion Simulation](https://mlsanity.com/paper/2603.10474/) (2026)
- [Coupled Distributional Random Expert Distillation for World Model Online Imitation Learning](https://mlsanity.com/paper/2505.02228/) (2025)
- [Imitation Learning from Human Motion Alone Does Not Guarantee Biomechanically Plausible Gait Kinetics](https://mlsanity.com/paper/2603.12408/) (2026)
- [Learning a Speed-adaptive Hip Exoskeleton Control Policy Via Sim-to-real Reinforcement Learning](https://mlsanity.com/paper/2609.28027/) (2026)

## Abstract (arXiv)

Virtual models of human gait, or digital twins, offer a promising solution for studying mobility without the need for labor-intensive data collection. However, challenges such as the sim-to-real gap and limited adaptability to diverse walking conditions persist. To address these, we developed and validated a framework to create a skeletal humanoid agent capable of adapting to varying walking speeds while maintaining biomechanically realistic motions. The framework combines a synthetic data generator, which produces biomechanically plausible gait kinematics from open-source biomechanics data, and a training system that uses adversarial imitation learning to train the agent's walking policy. We conducted comprehensive analyses comparing the agent's kinematics, synthetic data, and the original biomechanics dataset. The agent achieved a root mean square error of 5.24 +- 0.09 degrees at varying speeds compared to ground-truth kinematics data, demonstrating its adaptability. This work represents a significant step toward developing a digital twin of human locomotion, with potential applications in biomechanics research, exoskeleton design, and rehabilitation.