Optimizing Contextual Ergonomics Models in Human-Robot Interaction

Current ergonomic assessment procedures require observation and manual annotation of postures by an expert, after which ergonomic scores are inferred from these annotations. Our aim is to automate this procedure and to enable robots to optimize their behavior with respect to such scores. A particular challenge is that ergonomic scoring requires accurate biomechanical simulations which are computationally too expensive to use in robot control loops or optimization. To address this, we learn Contextual Ergonomics Models, which are Gaussian Process Latent Variable Models that have been trained with full musculoskeletal simulations for specific tasks contexts. Contextual Ergonomics Models enable search in a low-dimensional latent space, whilst the cost function can be defined in terms of the full high-dimensional musculoskeletal model, which can be quickly reconstructed from the latent space. We demonstrate how optimizing Contextual Ergonomics Models leads to significantly reduced muscle activation in an experiment with eight subjects performing a drilling task.

Authors

• Antonio Gonzales Marin
• Mohammad S. Shourijeh
• Pavel E. Galibarov
• Michael Damsgaard
• Lars Fritzsch
• Freek Stulp

Keywords

• Ergonomics
• Biological system modeling
• Context modeling
• Task analysis
• Data models
• Muscles
• Human-robot Interaction
• Latent Variables
• Muscle Activity
• Latent Space
• Gaussian Process
• Assessment Procedures
• Control Loop
• Manual Annotation
• Robot Control
• Latent Variable Model
• Musculoskeletal Model
• Training Data
• Part Of Process
• Open-source Software
• Human Model
• Alternative Models
• Upper Limb
• Musculoskeletal Disorders
• Valid Approach
• Muscle Groups
• Joint Moments
• Higher Muscle Activity