Sim-to-real transfer gets you 80–95% of the way. The remaining gap is unpredictable—surface friction, joint wear, payload variance, environmental drift. Nervous Machine learns each robot's real-world signature and closes the gap at the edge.
Digital twins and simulations train on idealized physics. But every deployed robot encounters conditions the simulator never modeled—surface irregularities, temperature shifts, mechanical wear, load variations.
Standard approaches retrain on cloud-collected data, which is slow, expensive, and ignores the fact that each robot's drift is unique. Robot #47 on floor 3 doesn't have the same joint wear as robot #12 on floor 1. Population-level retraining can't fix instance-level divergence.
Nervous Machine enriches your simulation priors with causal models that update from each robot's operational reality. The system learns which factors are drifting, by how much, and whether the drift indicates a missing causal factor or gradual degradation.
When the fleet shares validated causal vectors (~1KB), every robot benefits from collective learning without exposing raw telemetry. Robot #47's discovery propagates to the fleet. The fleet gets smarter. No cloud retraining required.
The framework runs on-device, learning each robot's divergence from its simulation baseline and adapting in real time.
Continuously measure the gap between predicted and actual dynamics. Learn which simulation assumptions break in deployment and by how much.
Distinguish between environmental noise and genuine mechanical degradation. High certainty + persistent error triggers a curiosity signal—something has changed.
Validated causal relationships propagate across the fleet. A discovery on one robot improves predictions for all robots in similar configurations.
Runs on the robot's existing compute—ARM, MCU, or embedded GPU. No cloud dependency. Learning happens where the physics happens.
Whether you're deploying warehouse AMRs, industrial arms, or field robots, Nervous Machine adapts each one to its actual operating conditions.