Multi-Performer Motion Capture for Collaborative Robotics
We capture synchronized multi-performer actions with high precision, including cooperative movement, object handovers, shared manipulation and coordinated task execution, delivering structured interaction data for collaborative robotics, human-robot interaction, multi-agent learning, embodied AI and Physical AI workflows.
Capture synchronized multi-human interactions as structured motion references that preserve relative body transforms, joint-level kinematics, contact states, object trajectories, task timing, spatial relationships and role-based actions across cooperative scenarios. Multi-performer sequences can be organized around handover events, shared manipulation, coordinated locomotion, leader–follower behaviour and synchronized task phases, giving robotics teams a richer representation of how multiple agents interact in the same environment. This motion can support multi-agent imitation learning, collaborative policy training, human–robot interaction, cooperative manipulation, behaviour modeling, coordination research and Physical AI simulation, where timing, contact and inter-agent relationships are as important as the individual motion itself.