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Ergonomics is a key factor to consider when designing control architecturesfor effective physical collaborations between humans and humanoid robots. Incontrast, ergonomic indexes are often overlooked in the robot design phase,which leads to suboptimal performance in physical human-robot interactiontasks. This paper proposes a novel methodology for optimizing the design ofhumanoid robots with respect to ergonomic indicators associated with theinteraction of multiple agents. Our approach leverages a dynamic and kinematicparameterization of the robot link and motor specifications to seek for optimalrobot designs using a bilevel optimization approach. Specifically, a geneticalgorithm first generates robot designs by selecting the link and motorcharacteristics. Then, we use nonlinear optimization to evaluate interactionergonomy indexes during collaborative payload lifting with different humans andweights. To assess the effectiveness of our approach, we compare the optimaldesign obtained using bilevel optimization against the design obtained usingnonlinear optimization. Our results show that the proposed approachsignificantly improves ergonomics in terms of energy expenditure calculated intwo reference scenarios involving static and dynamic robot motions. We plan toapply our methodology to drive the design of the ergoCub2 robot, a humanoidintended for optimal physical collaboration with humans in diverse environments

Codesign of Humanoid Robots for Ergonomy Collaboration with Multiple Humans via Genetic Algorithms and Nonlinear Optimization