The end-to-end platform for recursive co-improvement of world models and embodied agents — from virtual simulation to physical deployment.
A unified software stack that combines recursive co-improvement algorithms with high-performance infrastructure. Train world models and embodied agents together, validate in simulation, and deploy across diverse robot embodiments.
Connect and manage multiple robot embodiments through a unified interface. Support for quadrupeds, humanoids, manipulators, and custom hardware.
Generate, edit, and validate interactive 3D environments. Fine-tune physical correctness and automatically generate curricula for agent training.
Run recursive co-improvement loops at scale with distributed RL, asynchronous rollouts, and automatic failure-driven curriculum updates.
Benchmark agent capabilities across virtual and real scenarios. Diagnose failures, attribute root causes, and track improvement over iterations.
Deploy policies with full-duplex, low-latency inference. Optimized for edge devices and cloud coordination with model compression and kernel tuning.
Seamlessly transfer policies from simulation to reality. Continuously collect real-world feedback and feed it back into the next RCI iteration.
Train general-purpose manipulation and navigation policies that adapt to new SKUs, layouts, and workflows without re-collecting large real-world datasets.
Deploy robots that learn assembly, inspection, and material handling tasks through simulation, then refine skills with minimal real-world fine-tuning.
Generate high-fidelity interactive environments for AI training, scenario testing, and digital twin applications in gaming, robotics, and defense.
Build home and service robots that generalize across environments, safely interact with humans, and continuously improve from daily experience.
Request early access to the RCI Platform, schedule a technical demo, or discuss a private deployment for your organization.