A frontier AI company building a robot foundation model that enables robots to learn new tasks from one demonstration without further training, using training conducted entirely in simulation.
Primary use case: Enabling robots, including robot arms, to quickly learn and perform new tasks from demonstrations.
Locomotion
unknown
Hand Type
unknown
Autonomy Level
unknown
AI Capabilities
Learning new tasks from a single demonstration without further training, Instant teaching, Instant generalization, Instant reactivity, Instant failure detection, Instant recovery, Training entirely in simulation, Scaling through compute
Availability
Early access is planned, with registration of interest currently available.
Primary Use Case
Enabling robots, including robot arms, to quickly learn and perform new tasks from demonstrations.
ABSTRACT Autonomous navigation in complex, unstructured environments poses a significant challenge, with traditional planners lacking adaptability and end-to-end learning methods hindered by data dependency or training instability. Learning-based hybrid methods that automatically tune traditional planner parameters are a promising compromise, but we identify an underlying architectural limitation that contributes to constraining their performance: a low-frequency, high-latency control loop. This paper resolves this architectural bottleneck by proposing a multi-rate, hybrid hierarc
H3C Delivers High-Performance Connectivity for the World Humanoid Robot Games
Science Robotics, Volume 11, Issue 117, August 2026.