Abundance

Inspiration: Humanoid robots are hitting production scale. Tesla, Figure AI, 1X, Boston Dynamics are all racing to deploy. The bottleneck is not compute or hardware. It is training data. Real-world collection is too slow, too dangerous, and physically impossible for the full range of scenarios robots need to learn. Simulation tools exist but are built for clean structured environments, not the messy and unpredictable conditions robots actually operate in. Nobody was treating that gap as a product opportunity.

What it does: Abundance generates physics-accurate synthetic video training data for humanoid robots at any scale. Give us a task, environment, and robot morphology and we output thousands of labeled, physics-consistent video clips covering fine motor skills, dangerous edge cases, and every scenario in between, delivered directly into a lab's training pipeline.

How we built it: Three-stage pipeline. Rigid-body physics simulation generates ground-truth trajectories and contact events. A 3D spatial model places robot and environment geometry with accurate material properties. A fine-tuned generative video model renders photorealistic output with domain randomization baked in. Physics first, pixels second.

Challenges we ran into: Getting video realistic enough to fool downstream vision models without hallucinating contact physics. Simulation tools are built for clean structured environments, not the unpredictable surfaces, variable lighting, and object behavior robots face in the real world. Maintaining temporal consistency across frames during occlusion. Balancing domain randomization breadth against training signal coherence.

Accomplishments that we're proud of: End-to-end pipeline running live from task spec to labeled video output. Automatic ground-truth labeling of finger contact, weight transfer, and object interaction events. Output compatible with Diffusion Policy and LeRobot out of the box, so integration time for a lab is near zero.

What we learned: Generative models do not understand Newtonian physics. They fake it convincingly until a robot tries to learn from it. The only reliable architecture is physics-first, pixels-second. That insight rewired the entire stack.

What's next for Abundance: Onboard our first design partner and deliver an initial dataset. Get one robotics lab to run a training experiment and report a measurable policy improvement number. That single result opens every subsequent conversation. From there, expand to full-body locomotion sequences and surgical robotics data, and raise our seed round on the back of that first validation.

Built With

• amazon-web-services
• next.js
• react
• tailwind
• vercel