  Ergonomics and human comfort are essential concerns in physical human-robot
interaction applications. Defining an accurate and easy-to-use ergonomic
assessment model stands as an important step in providing feedback for postural
correction to improve operator health and comfort. In order to enable efficient
computation, previously proposed automated ergonomic assessment and correction
tools make approximations or simplifications to gold-standard assessment tools
used by ergonomists in practice. In order to retain assessment quality, while
improving computational considerations, we introduce DULA, a differentiable and
continuous ergonomics model learned to replicate the popular and scientifically
validated RULA assessment. We show that DULA provides assessment comparable to
RULA while providing computational benefits. We highlight DULA's strength in a
demonstration of gradient-based postural optimization for a simulated
teleoperation task.
