From perception to action — vision-language and vision-language-action models, on real robots, with a deterministic safety boundary.
Hyperion does not position itself as a general engineering bench. Technical fluency is used to make better product decisions, challenge assumptions, define evidence and coordinate the critical specialists required by the mandate.
Perception
Policy (VLA)
Control
Safety monitor
From sensors to motion, behind an independent safety monitor.
A four-stage pipeline: perception, then the policy (a vision-language-action model), then control, then an independent safety monitor.
A robot that works in a demo is not one you can deploy. The consequential choices — VLM or VLA, what a simulation is allowed to prove, where the deterministic safety boundary sits — are product decisions before they are engineering ones. Hyperion owns them, names the evidence that closes each one, and coordinates the specialists who implement the result.
The sensor-to-action pipeline (perception → policy → control), the VLM-vs-VLA split (a VLM perceives and reasons; a VLA outputs actions), a training and evaluation loop with simulation and sim-to-real, and a deterministic safety boundary — the policy proposes, an independent monitor constrains.
ROS 2; vision-language-action policies (π-family, SmolVLA, NVIDIA Isaac GR00T) and VLMs; LeRobot for data, training and evaluation; teleoperation for demonstrations; simulation (Isaac Sim/Lab) and sim-to-real; fine-tuning (LoRA/QLoRA) and closed-loop evaluation; runtime safety monitor, operating envelope and emergency stop.
Perception to action, with a boundary that fails safe
Learned models propose; a deterministic boundary disposes. Every motor command passes an independent safety monitor before it reaches hardware.
Input
Deterministic
Learned
Safety
Output
Flow from sensors through perception, VLM and VLA, planner and controller, to an independent safety monitor that gates the actuators, with feedback to the sensors.