The Labs’ perception programs give machines the intelligence to identify people, objects, and scenes. Human analysis allows machines to detect and track human movements, including specific gesture and activity recognition, affect recognition and face recognition. Similarly, our object recognition can detect and track moving objects, as well as classify objects and conduct pose estimation. Vecna’s sensor fusion and world modeling technology also supports autonomous navigation through location recognition, 3D reconstruction, and SLAM.

Vecna’s navigation system gives robots the ability to safely and efficiently negotiate unstructured environments. The Labs team has developed a context-aware navigation system that uses sensor fusion and world modeling technology to plan paths among dynamic obstacles. In addition, the system supports optimal path planning, person following, autonomous docking to charging stations, and topological planning.

Vecna develops machines dexterous enough to handle small parts and strong enough to lift several hundred pounds. The Labs leverage hydraulics to create arms that are strong and fast while remaining lightweight and energy-efficient. These systems are equipped with advanced autonomy, including autonomous grasp selection, collision-free motion planning, and multi-sensor guided movement making them easy to use and safe around human workers.

Vecna’s team develops technology that coordinates fleets of autonomous systems. Our system performs multi-agent resource allocation, constrained optimization, task planning and scheduling, route generation, and execution monitoring. It coordinates agents to work independently, freeing operators to oversee groups of robots rather than commanding low-level tasks for each individual unit.

Usability is key to advanced technologies. Vecna Labs strives to create intuitive interfaces for machine supervision, including control by mobile and gesture interaction. Vecna’s machines measure human response through passive and automatic methods, including through machine vision and learning. Intention expression also conveys a robot’s internal state and can help guide human interactions.