Manipulation

Bimanual Manipulation

Bimanual manipulation is the coordinated use of two robot arms to perform tasks impossible or awkward for one, including stabilizing an object with one hand while the other acts on it, stretching deformables, re-grasping, and handling large items. Coordination modes range from symmetric and leader-follower motions to fully asymmetric role division, and the doubled action space with tight inter-arm constraints makes both planning and learning markedly harder than single-arm settings.

Why it matters for physical AI

Most human manual work is two-handed, so bimanual competence gates a large share of real tasks, and systems like ALOHA showed learned policies can master coordination that defeated analytic approaches.