# Ergonomically Intelligent Physical Human-Robot Interaction: Postural Estimation, Assessment, and Optimization

arXiv: https://arxiv.org/abs/2108.05971 · HTML: https://mlsanity.com/paper/2108.05971/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 52 references that are in the map.
- First posted on arXiv 12 Aug 2021.

## Builds on (most cited first, 7 of 7)

- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [OpenPose: Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields](https://mlsanity.com/paper/1812.08008/) (2018)
- [Markerless tracking of user-defined features with deep learning](https://mlsanity.com/paper/1804.03142/) (2018)
- [Real-time Robot-assisted Ergonomics](https://mlsanity.com/paper/1805.06270/) (2018)
- [Data-Driven Approach to Simulating Realistic Human Joint Constraints](https://mlsanity.com/paper/1709.08685/) (2017)
- [Planning for Muscular and Peripersonal-Space Comfort during Human-Robot Forceful Collaboration](https://mlsanity.com/paper/1807.11323/) (2018)
- [DULA: A Differentiable Ergonomics Model for Postural Optimization in Physical HRI](https://mlsanity.com/paper/2107.06875/) (2021)

## Built on by (most cited first, 1 of 1)

- [Occlusion-Robust Multi-Sensory Posture Estimation in Physical Human-Robot Interaction](https://mlsanity.com/paper/2208.06494/) (2022)

## Abstract (arXiv)

Ergonomics and human comfort are essential concerns in physical human-robot interaction. Common practical methods in the area either fail in estimating the correct posture due to occlusion or suffer from inaccurate ergonomics models in performing postural optimization. We propose a novel alternative framework for posture estimation, assessment, and optimization for ergonomically intelligent physical human-robot interaction. We show that we can estimate human posture solely from the trajectory of the interacting robot with median deviation of 5 deg from motion capture. We propose DULA, a differentiable ergonomics assessment tool with 99.73% accuracy comparing to RULA. We use DULA in postural optimization for physical human-robot interaction tasks such as co-manipulation and teleoperation. We evaluate our framework through human and simulation experiments.