Space logistics and transportation.
AI, Physical AI, and Embodied AI are opening new efficiencies, new materials, and new optimizations across space transportation and the management of space logistics. Reaching a destination in space is a velocity budget, and the whole chain, from launch through orbital transfer, rendezvous, capture, servicing, assembly, and return, becomes tractable when reusable, shared infrastructure and autonomous in-space transport pay that budget down. A momentum-exchange tether catch, a launch loop held steady by its controller, an electromagnetic sled handoff, a rendezvous with a tumbling target: each is an estimator resolving state and a controller holding a proven corridor, run on the vehicle at the edge. This track is the research into what those capabilities are and how to test, apply, and deploy them, so the space economy can expand, open, and decentralize.
Browse the instrument suite →orbital-logistics on GitHub ↗Pick a destination and compose the chain: reusable infrastructure and autonomous in-space transport pay down the velocity budget the launch vehicle would carry alone. The mass-ratio math is the exact rocket equation; the Δv budgets are representative.
In the field · autonomous rendezvous and proximity operations already run in orbit. Astroscale's ADRAS-J approached a non-cooperative three-tonne rocket body, held station within fifteen meters, flew around it, and validated autonomous collision avoidance; Northrop Grumman's SpaceLogistics Mission Extension Vehicles docked with client satellites in geostationary orbit to extend their lives. These are the first links of an in-space logistics layer. The research here is the autonomy that lets one estimation-and-control stack serve every link in the chain, from a launch handoff to a non-cooperative capture, priced in Δv and joules and carried on the vehicle.
↓ Whitepaper · PDFRead online◆ Living paperTechnical Report TR-2026-27 · Institute for Physical AI @ BMI