# Data-Driven Approach to Simulating Realistic Human Joint Constraints

arXiv: https://arxiv.org/abs/1709.08685 · HTML: https://mlsanity.com/paper/1709.08685/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Sep 2017.

## Built on by (most cited first, 6 of 6)

- [Learning to Manipulate Amorphous Materials](https://mlsanity.com/paper/2103.02533/) (2021)
- [DULA: A Differentiable Ergonomics Model for Postural Optimization in Physical HRI](https://mlsanity.com/paper/2107.06875/) (2021)
- [Ergonomically Intelligent Physical Human-Robot Interaction: Postural Estimation, Assessment, and Optimization](https://mlsanity.com/paper/2108.05971/) (2021)
- [Modeling human intention inference in continuous 3D domains by inverse planning and body kinematics](https://mlsanity.com/paper/2112.00903/) (2021)
- [Learning Realistic Joint Space Boundaries for Range of Motion Analysis of Healthy and Impaired Human Arms](https://mlsanity.com/paper/2311.10653/) (2023)
- [Occlusion-Robust Multi-Sensory Posture Estimation in Physical Human-Robot Interaction](https://mlsanity.com/paper/2208.06494/) (2022)

## Abstract (arXiv)

Modeling realistic human joint limits is important for applications involving physical human-robot interaction. However, setting appropriate human joint limits is challenging because it is pose-dependent: the range of joint motion varies depending on the positions of other bones. The paper introduces a new technique to accurately simulate human joint limits in physics simulation. We propose to learn an implicit equation to represent the boundary of valid human joint configurations from real human data. The function in the implicit equation is represented by a fully connected neural network whose gradients can be efficiently computed via back-propagation. Using gradients, we can efficiently enforce realistic human joint limits through constraint forces in a physics engine or as constraints in an optimization problem.