The Foundation Model Built for Harsh Environments

TorqueField is a foundation model purpose-built for field operations at scale, from crop counting and selective harvesting to heavy equipment control and worksite safety.

3D illustration of a crop field with agricultural machinery, representing TorqueField, a foundation model designed for large-scale field operations.

24/7

Runtime stability real-time edge-native

~50

Examples only, deploy in weeks

99.9%

Performance in real outdoor conditions

Real Fields, Real Variability

Outdoor operations are defined by constant variability across terrain, lighting, weather, and the materials machines interact with.

A tractor passing through a field, with dust

Harsh environments

artificial intelligence counting the number of fruits on a tree

Complex reasoning

Part of a piece of large-scale equipment

Equipment intelligence

Professionals at a construction site, wearing appropriate clothing and hard hats, near a tractor.

Situational awareness

TorqueField Use Cases

Built to Integrate With the Robots You're Already Running

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Built for deployment across sensors and edge compute platforms

TorqueField is designed to run where data is collected. The model operates across RGB cameras, thermal imagers, lidar, radar, multispectral sensors, and fused sensing systems, delivering low-latency reasoning directly at the edge. It integrates with drones, vehicles, fixed monitoring infrastructure, mobile platforms, and industrial equipment, enabling scalable deployment across large sites and distributed operations. Runs on Orin, Thor, RTX, and custom SoCs.

Reasoning Across Space and Time in the Field

TorqueField combines perception, physical understanding, and spatio-temporal reasoning to model how terrain and conditions evolve in outdoor environments. This allows the system to generalize across new crops, sites, and field operations with far less task-specific data than traditional approaches.

Modular deployment icon

Modular or End-to-End

Run TorqueField as a full robotics stack, or drop in just the pieces you need: perception, physical reasoning, or planner cost functions. It fits the architecture you already have.

Industrial robotic arm manipulating an object

Physics-reasoning AI

Because TorqueField reasons about physics instead of memorizing examples, it learns new crops, sites, and tasks with 1000x less training data than conventional approaches. More done with less data.

Edge compute device for on-device robotics inference

Edge-Native & Ready to use Hardware Kits

Built to run on the edge, on the equipment itself, TorqueField is available as ready to use hardware kits.

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