At HYPRLABS, we believe robots learn best when they learn as they move.

HYPRDRIVE is that belief in action.

We’ve pioneered a new class of self-learning autonomy — a suite of algorithms, techniques, and pipelines¹ that require no supervised labeling, no camera calibration, no simulation, and no HD maps. Instead, HYPRDRIVE enables robots to learn interactively from their own experience, continuously optimizing their understanding of themselves and their environment as they move through the world.

We call this technique Immersive Learning.

01 FOUNDATIONAL LEARNING

The process begins with a human-operated robot— in this case, a car. As the human drives, HYPRDRIVE acquires the foundational dataset, seeding the AI with a transformer-based, end-to-end pixels-to-action conditional imitation learning frame of reference.

This dataset provides behavioral priors not through rule-based heuristics or synthetic scenes, but from real-world sensory and actuator data that directly encode both human driving intelligence and the robot’s own dynamics.

02 HYBRID LEARNING

Once the foundational model is trained, development transitions into hybrid mode — where the AI drives under human supervision. When the AI makes a suboptimal decision, the driver can correct it in real time, providing exactly the feedback gap the model needs to learn. We call this Guidance Feedback (GF) — a practical form of in-situ Reinforcement Learning from Human Feedback (RLHF).

Simultaneously, sensor data paired with synchronized vehicle telemetry is encoded into the latent vector space that the neural network operates in. Through latent-space density estimation, the system identifies underrepresented or high-entropy scenarios — the rare moments that truly expand the model’s capability.

These high-value samples, together with Guidance Feedback corrections, are compressed into the model’s latent space and efficiently streamed to the cloud.²

This approach redefines fleet-scale learning by filtering for value, not volume — shifting the paradigm from “collect everything” to “collect only what’s meaningful.”

03 CONTINUOUS LEARNING

In the cloud, our framework receives asynchronous correction packets from the fleet — compressed latent embeddings of environmental novelty or behavioral micro-gradients.

The foundational model is fine-tuned with these updates in iterative epochs. Once regression tests pass, only the changed model parameters are transmitted back to the fleet — representing roughly a 90% reduction in data transmission.

This closed-loop process runs continuously, enabling rapid, safe, and efficient fleet-wide adaptation. The model then prunes redundant embeddings to compactify the dataset, ensuring every vector contributes uniquely to learning velocity.