Research
Making compute in orbit feasible.
RotaStellar Research builds the systems and models behind orbital compute - constraint-aware planning, on-satellite execution, and distributed training across Earth and space. We publish what we learn: papers, open models, datasets, and benchmarks.
The planner, in one request
CAE turns a satellite and a workload into a feasible plan - or an explicit reason it can't be done. Hover any field to see what it means.
rotastellar-cae
$ curl https://api.rotastellar.com/v1/plan \ -H "Content-Type: application/json" \ -d '{ "satellite_id": "25544", "preset_id": "onboard-ml-inference" }' # CAE returns a constraint-aware plan { "satellite": { "name": "ISS (ZARYA)", "altitude_km": 408 }, "preset": { "id": "onboard-ml-inference", "steps": 4 }, "orbital_environment": { "eclipse_fraction": 0.35 }, "plan": { "total_duration_s": 2700, "windows_used": 8, "status": "scheduled" }, "error_budget": { "delivery_confidence": 0.98 } }
Papers
All papers → Constraint-Aware Execution Planning for Hybrid Space-Ground Compute Workloads
A planning system that produces feasible execution plans for hybrid space-ground compute workloads under power, thermal, communication, and compute constraints. Deployed in production as the CAE API.
ML Approaches for Orbital Conjunction Analysis
Real-time Satellite Tracking at Scale
Open resources
Models
Pre-trained model families for orbital intelligence and distributed compute.
Datasets
Computed and simulated datasets for orbital research.
Benchmarks
Held-out evaluation suites for orbital ML and distributed compute.
Open source
All projects → rotastellar-track
High-performance satellite tracking library
rotastellar-intel
Orbital intelligence and analysis tools
Work with us.
We collaborate with researchers and operators on orbital compute, scheduling, and on-satellite ML. If your work touches any of these, we'd like to hear from you.