Research · June 2026
Reinforcement Learning & Robotics Testbed
Training control policies on our GPU cluster and moving them onto physical robotic hardware — the ground floor of autonomous systems.
The autonomy we write about in our mission areas has to start somewhere physical. This is it.
Our robotics program pairs deep reinforcement learning — policies trained in simulation on our on-premises GPU cluster — with real hardware, working toward controllers that transfer from sim to the physical world.
Why it matters
Every long-term SDI mission — debris capture, autonomous manufacturing, supply-chain drones — reduces to the same core competency: machines that perceive, decide, and act without a human in the loop. Reinforcement learning on our own compute is how we build that muscle now, on hardware we can afford to crash.
Status
Early-stage research, actively underway.