# Generative GaitNet

arXiv: https://arxiv.org/abs/2201.12044 · HTML: https://mlsanity.com/paper/2201.12044/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 64 references that are in the map.
- First posted on arXiv 28 Jan 2022.

## Builds on (most cited first, 10 of 10)

- [Emergence of Locomotion Behaviours in Rich Environments](https://mlsanity.com/paper/1707.02286/) (2017)
- [DeepMimic: Example-Guided Deep Reinforcement Learning of Physics-Based Character Skills](https://mlsanity.com/paper/1804.02717/) (2018)
- [Teacher algorithms for curriculum learning of Deep RL in continuously parameterized environments](https://mlsanity.com/paper/1910.07224/) (2019)
- [Learning Symmetric and Low-energy Locomotion](https://mlsanity.com/paper/1801.08093/) (2018)
- [Learning Locomotion Skills Using DeepRL: Does the Choice of Action Space Matter?](https://mlsanity.com/paper/1611.01055/) (2016)
- [Synthesis of Biologically Realistic Human Motion Using Joint Torque Actuation](https://mlsanity.com/paper/1904.13041/) (2019)
- [Learning to Run challenge solutions: Adapting reinforcement learning methods for neuromusculoskeletal environments](https://mlsanity.com/paper/1804.00361/) (2018)
- [Artificial Intelligence for Prosthetics - challenge solutions](https://mlsanity.com/paper/1902.02441/) (2019)
- [Interactive Modelling of Volumetric Musculoskeletal Anatomy](https://mlsanity.com/paper/2106.05161/) (2021)
- [Functionality-Driven Musculature Retargeting](https://mlsanity.com/paper/2007.15311/) (2020)

## Built on by (most cited first, 11 of 11)

- [DynSyn: Dynamical Synergistic Representation for Efficient Learning and Control in Overactuated Embodied Systems](https://mlsanity.com/paper/2407.11472/) (2024)
- [KINESIS: Motion Imitation for Human Musculoskeletal Locomotion](https://mlsanity.com/paper/2503.14637/) (2025)
- [Exo-Plore: Exploring Exoskeleton Control Space through Human-aligned Simulation](https://mlsanity.com/paper/2601.22550/) (2026)
- [Natural and Robust Walking using Reinforcement Learning without Demonstrations in High-Dimensional Musculoskeletal Models](https://mlsanity.com/paper/2309.02976/) (2023)
- [Scalable Exploration for High-Dimensional Continuous Control via Value-Guided Flow](https://mlsanity.com/paper/2601.19707/) (2026)
- [Bidirectional GaitNet: A Bidirectional Prediction Model of Human Gait and Anatomical Conditions](https://mlsanity.com/paper/2306.04161/) (2023)
- [From 3D Pose to Prose: Biomechanics-Grounded Vision--Language Coaching](https://mlsanity.com/paper/2603.26938/) (2026)
- [Exciting Action: Investigating Efficient Exploration for Learning Musculoskeletal Humanoid Locomotion](https://mlsanity.com/paper/2407.11658/) (2024)
- [MUSIC: Learning Muscle-Driven Dexterous Hand Control](https://mlsanity.com/paper/2604.23886/) (2026)
- [Real-Time Forecasting of Pathological Gait via IMU Navigation: A Few-Shot and Generative Learning Framework for Wearable Devices](https://mlsanity.com/paper/2405.09569/) (2024)
- [Exoskeleton Control through Learning to Reduce Biological Joint Moments in Simulations](https://mlsanity.com/paper/2603.07629/) (2026)

## Abstract (arXiv)

Understanding the relation between anatomy andgait is key to successful predictive gait simulation. Inthis paper, we present Generative GaitNet, which isa novel network architecture based on deep reinforce-ment learning for controlling a comprehensive, full-body, musculoskeletal model with 304 Hill-type mus-culotendons. The Generative Gait is a pre-trained, in-tegrated system of artificial neural networks learnedin a 618-dimensional continuous domain of anatomyconditions (e.g., mass distribution, body proportion,bone deformity, and muscle deficits) and gait condi-tions (e.g., stride and cadence). The pre-trained Gait-Net takes anatomy and gait conditions as input andgenerates a series of gait cycles appropriate to theconditions through physics-based simulation. We willdemonstrate the efficacy and expressive power of Gen-erative GaitNet to generate a variety of healthy andpathologic human gaits in real-time physics-based sim-ulation.