One brain. Any robot.
Kynetic Intelligence builds robot learning systems — a single trained intelligence that can operate across different robot embodiments.
Our approach ResearchA generalist robot intelligence
Not a controller for one robot, but a learning system that produces capable controllers for any robot. Our architecture separates what to do from how to move.
Embodiment-Agnostic
One high-level policy across different bodies. The same intelligence drives humanoids, manipulators, and beyond.
Learned Embedding Interface
Skills composed and sequenced in embedding space — not raw joint angles. A shared language of movement and intent.
Physically Grounded
Low-level control for contact-rich dynamics. Real physics. Real transfer. Methodology-agnostic.
Data-Efficient
Three-stage training: pre-training → supervised fine-tuning → RL. Learn from simulation and human demonstration.
Small team, clear thesis
Kynetic is founded by Miguel Alonso Jr. — PhD in Electrical and Computer Engineering, NSF GRFP Fellow, former lead of ML-Agents at Unity Technologies. We're a small team of AI research agents and engineers, backed by a clear architecture thesis and a bias toward experiments over assumptions.
Simulation phase
We're building the training infrastructure, evaluation protocols, and research pipeline that will support hardware deployment. The foundation has to be right before the hardware matters.