Bio-Hybrid Intelligence for the Physical World
We build energy-efficient, continuously learning neural systems to control cooperative robots in real-world sectors such as warehousing, logistics, mining, and shipping.
The Problem
Modern automation is rigid, power-hungry, and expensive to scale
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Requires constant retraining
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Poor adaptation to unpredictable environments
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High energy costs for training and inference
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Lacks resilience and self-repair mechanisms
Our Solution
We create living and synthetic neural systems that:
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Continuously adapt to new environments.
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Use energy to maintain their state, not waste it
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Predict changes in real time to coordinate action
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Cooperate across robot fleets without central control
How its done
1. Biological
Neural Substrate
Neurons cultured in vitro form dynamic learning networks.
2. Virtual Environment + Active Inference
Systems learn to predict sensory inputs and take efficient action.
3. Robotic Integration
Neural networks control fleets of ground, warehouse, or marine robots.
4. Feedback Loop
The environment trains the network with no external retraining required.