mlsanityData to 7 Oct 2026

Generative GaitNet

arXiv 2201.12044 ↗First posted 28 Jan 2022cs.GR cs.LGcited by 11

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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.

Key facts. Cited by 11 papers in the map (data through 7 Oct 2026). Builds on 10 of its 64 references that are in the map. First posted on arXiv 28 Jan 2022.

Builds on

10 of 64 references.

References · 10

  1. 2017Emergence of Locomotion Behaviours in Rich Environmentscited by 159
  2. 2018DeepMimic: Example-Guided Deep Reinforcement Learning of Physics-Based Character Skillscited by 153
  3. 2019Teacher algorithms for curriculum learning of Deep RL in continuously parameterized environmentscited by 113
  4. 2018Learning Symmetric and Low-energy Locomotioncited by 53
  5. 2016Learning Locomotion Skills Using DeepRL: Does the Choice of Action Space Matter?cited by 44
  6. 2019Synthesis of Biologically Realistic Human Motion Using Joint Torque Actuationcited by 23
  7. 2018Learning to Run challenge solutions: Adapting reinforcement learning methods for neuromusculoskeletal environmentscited by 19
  8. 2019Artificial Intelligence for Prosthetics - challenge solutionscited by 12
  9. 2021Interactive Modelling of Volumetric Musculoskeletal Anatomycited by 8
  10. 2020Functionality-Driven Musculature Retargetingcited by 3

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 2; 2024: 3; 2025: 1; 2026: 5.

11 papers.

  1. 2024DynSyn: Dynamical Synergistic Representation for Efficient Learning and Control in Overactuated Embodied Systemscited by 12
  2. 2025KINESIS: Motion Imitation for Human Musculoskeletal Locomotioncited by 7
  3. 2026Exo-Plore: Exploring Exoskeleton Control Space through Human-aligned Simulationcited by 6
  4. 2023Natural and Robust Walking using Reinforcement Learning without Demonstrations in High-Dimensional Musculoskeletal Modelscited by 4
  5. 2026Scalable Exploration for High-Dimensional Continuous Control via Value-Guided Flowcited by 4
  6. 2023Bidirectional GaitNet: A Bidirectional Prediction Model of Human Gait and Anatomical Conditionscited by 3
  7. 2026From 3D Pose to Prose: Biomechanics-Grounded Vision--Language Coachingcited by 2
  8. 2024Exciting Action: Investigating Efficient Exploration for Learning Musculoskeletal Humanoid Locomotioncited by 1
  9. 2026MUSIC: Learning Muscle-Driven Dexterous Hand Controlcited by 1
  10. 2024Real-Time Forecasting of Pathological Gait via IMU Navigation: A Few-Shot and Generative Learning Framework for Wearable Devicescited by 0
  11. 2026Exoskeleton Control through Learning to Reduce Biological Joint Moments in Simulationscited by 0