AI &
Robotics Platform
The intelligence layer underlying every Foundation device: high-channel neural interfaces that read motor intent, adaptive AI systems that learn from each user, and the robotics platform that translates thought into movement.
Overview
Every bionic device the Foundation builds sits on the same foundational technology platform: a suite of neural interfaces, AI control systems, and robotic actuators developed in-house and refined continuously across all our research programs. The AI & Robotics Platform team is the common substrate that makes every other research area possible.
The core challenge we address is translation: how do you take signals from a person's nervous system — signals meant to move a biological limb that is no longer there, or that no longer responds — and turn them into precise, natural-feeling movement in a mechanical device? And how do you do this in a way that improves over time, that adapts to each individual user, and that works in the real world, not just in a controlled lab setting?
Our answer is a closed-loop system that reads intent (neural interface), interprets it (adaptive AI), executes it (robotic actuator), and reports back (sensory feedback) — continuously learning at every step.
Neural Interface Platform
High-Channel Cortical Interface
A high-channel-count cortical recording interface that captures motor intent directly from the primary motor cortex — enabling control of complex bionic movements without relying on residual peripheral nerve signals. Received FDA Breakthrough Device Designation in April 2026.
Peripheral Nerve Interface
A minimally invasive peripheral nerve interface providing high-fidelity motor intent signals from residual peripheral nerves — suitable for upper and lower limb prosthetic control, with substantially lower surgical burden than cortical approaches.
Sensory Feedback System
The sensory channel of our bidirectional interface: electrical microstimulation patterns delivered to sensory cortex or peripheral sensory nerves to convey grip force, texture, and limb position to prosthetic users — closing the sensorimotor loop.
Adaptive AI Control
Personalized Movement Model
Every Foundation device runs a personalized movement model that begins learning from first use and continuously refines its predictions of user intent — adapting to each individual's movement patterns, residual limb characteristics, and usage context. Devices improve measurably over the first 30 days of use.
Multi-Modal Intent Fusion
Rather than relying on a single input channel, our control system fuses signals from multiple modalities — neural recording, surface EMG, inertial measurement, and ambient computer vision — to produce more robust, natural intent interpretation in real-world conditions.
Federated Learning for Bionic Devices
A federated learning architecture that allows Foundation devices to learn from aggregate movement patterns across all users — improving the base model for everyone without sharing individual patient data.
Engineering Collaboration
We work with academic AI labs, robotics groups, neuroscience departments, and hardware engineering teams. If your work touches neural interfaces, adaptive control, or bionic actuation systems, let's talk.
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