Ergonomically Intelligent Physical Human-Robot Interaction: Postural Estimation, Assessment, and Optimization
This work addresses ergonomic challenges in physical human-robot interaction, offering a novel method for posture estimation and assessment, though it appears incremental as it builds on existing ergonomics models like RULA.
The paper tackles the problem of inaccurate posture estimation and ergonomics assessment in physical human-robot interaction by proposing a framework that estimates human posture from robot trajectories with a median deviation of 5 degrees and introduces DULA, a differentiable ergonomics tool with 99.73% accuracy compared to RULA, used for optimization in tasks like co-manipulation and teleoperation.
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.