4dlab generates robot datasets with force feedback to automate
assembly planning at millimeter precision using the laws of physics.
A four-stage physics simulation pipeline that converts unstructured mechanical designs into labeled robot training data at scale.
Accepts unstructured mechanical CAD files in any format. Parses geometry, material properties, and part relationships automatically.
High-fidelity simulation of part fitting, fastening, and mechanical interaction — capturing torque, friction, and contact forces.
Force-feedback signals, positional data, and assembly states are labelled and structured into standardized training schemas.
High-quality datasets feed directly into foundational AI models, reducing assembly planning time, cost, and error rates.
Every layer of the pipeline is designed to handle the complexity of real-world mechanical systems.
Simulates torque, friction, contact pressure, and insertion force across all mechanical joints — no physical rig required.
Physics EngineAccepts messy, heterogeneous CAD files from any source. Automatically resolves geometry, hierarchy, and material metadata.
Universal IngestGenerates richly annotated, standardized datasets in formats compatible with major robotics AI training frameworks.
Training ReadyFoundational models trained on 4dlab data reduce planning time, operational errors, and integration costs in robotic systems.
AI FoundationParallelized simulation infrastructure processes thousands of mechanical variants simultaneously — scales with your design library.
Cloud NativeFeedback loops allow rapid re-simulation as designs evolve, ensuring your AI models stay synchronized with product updates.
DevOps ReadyFrom industrial automation to advanced robotics research, our pipeline serves the full spectrum of precision assembly challenges.
4dlab's force-feedback datasets power Google Intrinsic's Flowstate platform, providing the structured training data needed for context-aware robotic assembly — reducing programming time and enabling robots to handle novel fastening tasks with minimal re-training.
Join engineering teams eliminating physical trials from their assembly AI workflows. Request early access to the 4dlab pipeline.