# Synthesis of Biologically Realistic Human Motion Using Joint Torque Actuation

arXiv: https://arxiv.org/abs/1904.13041 · HTML: https://mlsanity.com/paper/1904.13041/

## Key facts

- Cited by 23 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Apr 2019.

## Built on by (most cited first, 23 of 23)

- [Character Controllers Using Motion VAEs](https://mlsanity.com/paper/2103.14274/) (2021)
- [Neural Monocular 3D Human Motion Capture with Physical Awareness](https://mlsanity.com/paper/2105.01057/) (2021)
- [Differentiable Simulation of Soft Multi-body Systems](https://mlsanity.com/paper/2205.01758/) (2022)
- [DEP-RL: Embodied Exploration for Reinforcement Learning in Overactuated and Musculoskeletal Systems](https://mlsanity.com/paper/2206.00484/) (2022)
- [Latent Exploration for Reinforcement Learning](https://mlsanity.com/paper/2305.20065/) (2023)
- [AMP: Adversarial Motion Priors for Stylized Physics-Based Character Control](https://mlsanity.com/paper/2104.02180/) (2021)
- [A Survey on Reinforcement Learning Methods in Character Animation](https://mlsanity.com/paper/2203.04735/) (2022)
- [Generative GaitNet](https://mlsanity.com/paper/2201.12044/) (2022)
- [Learning to Get Up](https://mlsanity.com/paper/2205.00307/) (2022)
- [MyoDex: A Generalizable Prior for Dexterous Manipulation](https://mlsanity.com/paper/2309.03130/) (2023)
- [OstrichRL: A Musculoskeletal Ostrich Simulation to Study Bio-mechanical Locomotion](https://mlsanity.com/paper/2112.06061/) (2021)
- [Learning a Universal Human Prior for Dexterous Manipulation from Human Preference](https://mlsanity.com/paper/2304.04602/) (2023)
- [MS-MANO: Enabling Hand Pose Tracking with Biomechanical Constraints](https://mlsanity.com/paper/2404.10227/) (2024)
- [ASE: Large-Scale Reusable Adversarial Skill Embeddings for Physically Simulated Characters](https://mlsanity.com/paper/2205.01906/) (2022)
- [An Adaptable Approach to Learn Realistic Legged Locomotion without Examples](https://mlsanity.com/paper/2110.14998/) (2021)
- [Bidirectional GaitNet: A Bidirectional Prediction Model of Human Gait and Anatomical Conditions](https://mlsanity.com/paper/2306.04161/) (2023)
- [Muscles in Time: Learning to Understand Human Motion by Simulating Muscle Activations](https://mlsanity.com/paper/2411.00128/) (2024)
- [HFGaussian: Learning Generalizable Gaussian Human with Integrated Human Features](https://mlsanity.com/paper/2411.03086/) (2024)
- [Modeling human intention inference in continuous 3D domains by inverse planning and body kinematics](https://mlsanity.com/paper/2112.00903/) (2021)
- [Log2Motion: Biomechanical Motion Synthesis from Touch Logs](https://mlsanity.com/paper/2601.21043/) (2026)
- [MUSIC: Learning Muscle-Driven Dexterous Hand Control](https://mlsanity.com/paper/2604.23886/) (2026)
- [IAM: Identity-Aware Human Motion and Shape Joint Generation](https://mlsanity.com/paper/2604.25164/) (2026)
- [Where to Intervene: Action Selection in Deep Reinforcement Learning](https://mlsanity.com/paper/2507.04187/) (2025)

## Abstract (arXiv)

Using joint actuators to drive the skeletal movements is a common practice in character animation, but the resultant torque patterns are often unnatural or infeasible for real humans to achieve. On the other hand, physiologically-based models explicitly simulate muscles and tendons and thus produce more human-like movements and torque patterns. This paper introduces a technique to transform an optimal control problem formulated in the muscle-actuation space to an equivalent problem in the joint-actuation space, such that the solutions to both problems have the same optimal value. By solving the equivalent problem in the joint-actuation space, we can generate human-like motions comparable to those generated by musculotendon models, while retaining the benefit of simple modeling and fast computation offered by joint-actuation models. Our method transforms constant bounds on muscle activations to nonlinear, state-dependent torque limits in the joint-actuation space. In addition, the metabolic energy function on muscle activations is transformed to a nonlinear function of joint torques, joint configuration and joint velocity. Our technique can also benefit policy optimization using deep reinforcement learning approach, by providing a more anatomically realistic action space for the agent to explore during the learning process. We take the advantage of the physiologically-based simulator, OpenSim, to provide training data for learning the torque limits and the metabolic energy function. Once trained, the same torque limits and the energy function can be applied to drastically different motor tasks formulated as either trajectory optimization or policy learning. Codebase: https://github.com/jyf588/lrle and https://github.com/jyf588/lrle-rl-examples