Training assets for
robotic arms

Manipulation policies need objects with accurate mass, friction, and collision geometry. Rigyd generates physics-enabled assets optimized for grasping, stacking, and assembly training.

The problem

Why existing workflows fall short.

Grasping needs accurate contact physics

Robotic arm manipulation is highly sensitive to object mass, center of mass, friction coefficients, and collision mesh detail. Wrong values mean failed grasps.

Object diversity improves generalization

Training on a small set of objects produces brittle policies. Thousands of varied objects with realistic physics are needed for real-world robustness.

Center of mass matters

Asymmetric objects like tools, bottles, and electronics have off-center mass distributions. Ignoring this produces unrealistic simulation behavior.

How Rigyd helps

AI-native infrastructure that automates the hard parts.

Precise center of mass estimation

Rigyd analyzes object geometry and identified materials to estimate center of mass position — critical for manipulation planning.

Detailed collision meshes

Convex decomposition captures object geometry at the detail level needed for contact-rich manipulation, not just bounding box approximations.

Domain randomization ready

Physics estimates are calibrated within typical domain randomization variance ranges (mass within 15-20%), enabling robust policy training.

40%

better real-world performance with physics-accurate training

97%

faster than manual physics annotation

~5 min

per object from upload to sim-ready

Train better manipulation policies

Upload objects your robot needs to handle and get sim-ready assets in minutes.

Starts at $29/month. 30 sim-ready objects included.