Intelligence for the
Physical World.

Bridging the Sim-to-Real gap with expert-verified VLA trajectories. We provide the grounding needed for autonomous systems to operate safely in unstructured environments.

Embodied Reasoning

Our datasets test spatial awareness, long-horizon manipulation, and human-robot interaction norms in dynamic simulations.

Grounded datasets for
physical autonomous agents.

Providing the high-fidelity data needed to train Vision-Language-Action (VLA) models with production-grade reliability.

VLA Trajectories

Expert-verified trajectories testing spatial reasoning and multi-step manipulation across diverse robotic hardware.

Sim-to-Real Data

Specialized datasets for domain randomization and transfer learning, ensuring models adapt seamlessly from virtual to physical environments.

Navigation Bench

Pathfinding and obstacle avoidance data in high-density warehouse and domestic simulations with dynamic occlusions.

Robotics Benchmarks

Evaluating functional correctness, safety protocols, and operational reliability in high-stakes robotic scenarios.

Manipulator-Bench

Testing 7-DOF arm precision, force control, and adaptive grasping across 200+ unique objects.

Technical Specs

Spatial-IQ

Measures an agent's ability to interpret and reason about 3D space, occlusions, and dynamic obstacles in real-time.

Technical Specs

Safety-VLA Core

Rigorous testing of collision avoidance, human-robot interaction norms, and emergency protocols.

Technical Specs
FAQ

Common questions

Understanding our robotics evaluation protocols and Sim-to-Real validation methods.

We use domain randomization and physical realism tuning in Isaac Sim, coupled with expert-verified real-world trajectories for seamless transfer.

Our datasets support various form factors including 7-DOF arms, quadrupeds, and mobile manipulators.