Our Approach
Human-centric Physical AI. We focus on the data loop that makes robot learning actually scale — not the biggest platform, but the one teams can't replace.
The closed loop we optimize for
The Data Loop
The core challenge in robot learning isn't model size — it's data. Where it comes from, how it becomes usable, and how different sources combine. We build the closed loop that turns real-world failures into the next training round.
Real episode → Structured packet → Benchmark run → Failure replay → Back to training.
Once clients upload failure logs, get automatic replay and benchmark reports, and run policy A/B tests through our system, they start to depend on it. That's the moat.
What We Measure
Our north star isn't code volume or model size. It's these five numbers:
- New robot onboarding time — How fast can a new platform connect?
- New task to first baseline — From demonstration to runnable policy
- Single failure retraining time — How quickly does a failure re-enter the next training round?
- Automatic evaluation coverage — What % of decisions rely on our benchmark?
- Weekly client dependency — How many go/no-go decisions flow through our system?
Data Sources We Unify
Robot training data comes from five main paths. No single source is enough — the future is heterogeneous data combination.
- Internet human video — Scale and prior, but no action labels. We use it for task structure, not raw motor commands.
- Synthetic data — Automated generation, but Sim2Real gap. We focus on reward design and domain randomization.
- Motion capture — High precision, portable. Bridge between video and robot execution.
- Robot teleoperation — Most deployment-aligned, but expensive. We optimize for efficiency and RECAP-style correction flow.
- Heterogeneous combination — Cross-task, cross-robot, cross-modal. The real frontier.
Data representation matters more than raw volume. We turn episodes into structured packets, failures into training-ready cases, and benchmarks into decision surfaces.
Six Moats We Build
- Data moat — Not the most data, but the scarcest: real failures, corrections, eval history, cross-robot alignment.
- Benchmark moat — Clients' go/no-go decisions increasingly depend on our benchmark.
- Adapter moat — New robot and new input device onboarding speed as the strongest entry advantage.
- Workflow moat — Research, engineering, testing, and ops all see the same facts.
- Real–Sim correlation moat — Our benchmark results predict real-world performance.
- Commercial relationship moat — From "try this tool" to "we check your report daily, make decisions weekly."
Contact-Rich & Tactile
We specialize in contact-rich manipulation — insertion, assembly, force-sensitive tasks. Many teams do vision; the real closed loop for contact tasks is harder. We integrate tactile, torque, and force signals into the data loop and policy training.
Robot Learning Environment & Evaluation as a Service
Beyond "RL Environment as a Service," we offer Real-to-Sim-to-Real environment and evaluation cloud. Environment isn't just for running RL — it's for synthetic data, policy training, simulated evaluation, failure replay, and benchmark publishing. World model, environment generation, and evaluation are unified.
The ideal state: Clients upload real failure logs → we auto-generate replay and benchmark → all policy changes pass through our system first → clients check our regression report nightly → more robots and tasks onboard over time.
That's when we're not "a team that uses AI" — we're the default control plane for clients' real-world robot iteration.