Learning Functional Specialisation for Bimanual Robotic Control

Bimanual robotic control often requires diverse functional demands to be handled simultaneously under challenging control conditions. Modular controllers provide one way to distribute these demands, but how control responsibilities should be organised across controllers and the two arms, and how this organisation affects performance and flexibility, remains unclear. Biological motor control provides an organisational principle in which each hemisphere predominantly controls the contralateral limb, alongside functional specialisation and stable hand preferences. We therefore investigate whether analogous organisational principles can benefit modular robotic motor control in a musculoskeletal system with two communicating recurrent controllers. Using a $2\times 2$ design, we compare differential (controller-specific) and shared functional objectives together with fixed and balanced (role-varying) hand–task assignments. We find that task-aligned functional specialisation is strongest when differential objectives are combined with fixed hand–task assignment aligned with contralateral control routing. This condition achieves the highest performance and control efficiency on the task, reaching 81.33% bimanual success with significant improvements over the other training conditions. Its performance advantage over shared objectives increases under stronger motor noise but narrows under stronger energy penalties. It also shows lower hand-role flexibility: reversing the hand roles decreases bimanual success by 80.46 percentage points under differential objectives with fixed hand–task assignment, compared with only 1.51 percentage points under shared objectives with balanced hand–task assignment. Our findings show that, in bimanual robotic control with each controller more strongly routed to one hand, functional specialisation can develop through the organisation of learning responsibility and is associated with advantages in performance and control efficiency, whereas more distributed training objectives and task assignments favour greater interchangeability when roles change.