A swarm that moves anything in orbit needs a coordination brain, a body to act with, and a console to command it. We’re building the hardest and most defensible layer first: without the coordination, the robots are just more debris.
The coordination brain: how a swarm agrees, grips, and maneuvers a shared mass none of its robots could move alone, using only what each unit senses locally. This is the core technical risk, so it’s the piece we’ve built first, in high-fidelity simulation.
Our bet is that prediction is the mechanism behind good coordination. A Global State Prediction head lets each robot anticipate where the payload (and its neighbors) are about to be, and feeds that to the controller choosing actions. It’s the subject of the founder’s doctoral work, and it now runs: the head reaches a within-run prediction correlation around 0.85, reproduced across six independent seeds.
Every claim earns its place by an ablation: disable the component and watch the behavior degrade, or it doesn’t count. Getting there meant first killing the Q-value divergence that used to collapse runs late in training; a stabilized baseline (Huber loss, gradient and target clipping, reward scaling) made results repeatable, without which any comparison is just noise.
Four robots, a payload none of them could move alone, a gate they must thread, and a goal on the far side, with nothing but what each robot senses locally. This is collective transport through a gate: the setting where coordination stops being optional.
The gate is the unforgiving test: a constriction the swarm can only clear by collectively aligning and orienting the payload. No single robot can solve it from what it sees. This replay is one real successful episode, positions played straight from the training log.
This cylinder-through-a-gate is the lab distillation of the orbital job: the coordination kernel is what ships, and only the setting changes.
The founder’s PhD: each agent predicts the collective outcome of the team’s joint action, making a shifting world locally stationary. It beat independent (and even full-information) agents on less bandwidth, transferred sim-to-real on Khepera robots, and scaled 4→8 with no retraining.
We’re stress-testing that idea on the hardest regime (the gate, where coordination is unavoidable) to find which predicted quantity turns the substrate into a measurable gain. That is the live research below.
Many decentralized units, local sensing, a shared mass none can move alone, threaded through a constraint. The arena becomes 3D orbital dynamics, the cylinder a real vehicle, the robots gecko-pad ion servicers. The coordination problem is identical.
The head predicts the world well, but a head that predicts isn’t yet a head the policy uses. Whether prediction improves task success is the live question, and three levers are running now. Each is judged only by a prediction-ablation on matched task success, not by saliency alone.
Let the value loss shape the head (drop the frozen-feature detach). Does a head the policy can influence become a head the policy actually uses?
Predict progress-to-goal (the task-relevant quantity) instead of raw object motion. Does a more useful target lift coordination?
A neighbor’s force correlates ~0.33 with reward-to-go, versus ~0.00 for heading. Is force the thing worth anticipating?
Every curve below is a real training run, pulled straight from our experiment logs, not an illustration.
The servicer itself: a flat bus with a gecko-adhesive capture face, twin canted ion thrusters aft, and deployable solar wings, built to dock and thrust in swarms. A first-cut concept grounded in real orbital-servicing hardware.
The choices follow the mission. Gecko adhesion grips objects never designed to be captured, with no docking fixture or cooperative target required. Ion propulsion trades thrust for efficiency, so many small units can make patient, fuel-cheap orbit changes together. Deliberately, the coordination drives the design: the SV-class craft gets designed in simulation first (real geometry, actuation, and sensing) by a dedicated spacecraft designer.
The operations layer: one console tasking thousands of robots against thousands of objects: who moves what, when, and along which path. An operator selects a target from live orbital tracking, picks a maneuver (reorient, raise, or de-orbit) and assigns the swarm.
It’s already grounded in real data: the console renders live tracked objects from the public CelesTrak catalog, the same feed behind the tracking globe. That console is the Stage-1 deliverable, an operator sitting down with several assets and several payloads in orbit and tasking the swarm to act on them: a full simulated dry-run of the real product, before any metal is cut.