AMI Labs provides an SDK and runtime for action‑conditioned world models that transform high‑dimensional sensor streams (video, lidar, biosignals) into compact latent representations for safe, goal‑directed planning. The platform integrates with ROS, OPC‑UA, and wearable data formats, offering built‑in safety guardrails, continuous online learning, and scalable edge or cloud inference for autonomous robots, industrial automation, and medical wearables.
Funding
$1B raised to dateRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
AF+10Founders
Product
Problem
Current AI systems struggle to reliably interpret continuous, high‑dimensional sensor streams such as video, lidar, or physiological data because generative models focus on predicting raw observations, which are often noisy and unpredictable. This limits safety and controllability of autonomous agents in robotics, industrial automation, and healthcare.
Solution
AMI Labs develops action‑conditioned world models that learn compact, abstract representations of real‑world sensor inputs through self‑supervised learning. By operating predictions in a latent space, the models filter out low‑level noise and focus on task‑relevant dynamics, enabling agents to simulate the outcomes of candidate actions and select safe, goal‑directed sequences. The company provides a modular SDK and a cloud/edge runtime that integrate with common robotics middleware, industrial control protocols, and wearable device platforms. Safety guardrails are embedded in the planning layer to enforce domain‑specific constraints, and continuous‑learning modules keep the models up‑to‑date with sensor drift and evolving environments.
Target Audience
Primary customers are manufacturers of autonomous robots, industrial automation system integrators, and developers of medical‑grade wearable health technologies that require reliable, controllable AI for sensor‑driven decision making.
Features
- Self‑supervised multi‑modal representation learning pipeline that ingests camera, lidar, IMU, and biosignal streams to produce a low‑dimensional latent space
- Action‑conditioned forward model that predicts future latent states for any candidate control input
- Built‑in safety guardrails that enforce hard constraints such as collision avoidance or physiological limits during plan generation
- SDK with APIs for ROS, OPC‑UA, and standard wearable data formats for plug‑and‑play integration into robots, PLCs, and health devices
- Scalable inference engine deployable on cloud GPUs or edge accelerators for real‑time decision making
- Continuous online learning module that adapts the world model to sensor drift and changing environments
- Open‑source core libraries and accompanying research publications to encourage community validation and extension
- Monitoring dashboard that visualizes latent trajectories, predicted actions, and safety alerts for operators