Brandon Ong

Our recent work with CMU and Stanford, "Beyond Dodge Rate: Testing Humanoid Dodgeball Under Camera Failures," was accepted to the CoRL 2026 Workshop on RoboLetics.

Athletic sports are to robotics what video games are to LLMs: a safe, challenging arena for measuring how close machines actually are to human performance.

In sports, robots have to constantly adapt to the dynamics of the world while keeping up with the cognitive demands of the game.

We tested a Unitree G1 humanoid playing dodgeball in simulation and discuss how overlooking seemingly minute details in the design of evaluation settings can have an outsized impact on downstream success rates.

We hope this work brings us a step closer to safe, capable humanoid loco-manipulation. We're excited to share more soon about what we've been working on.

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data quality is cool i guess

we wrapped up yc s26 last week and a friend asked what lessons are worth passing on. here is a cleaned-up version of my answer:

1. team - travel for conferences, live together, have fun together. the more cohesive you are, the better your odds of success

2. community - every founder is an expert in their industry. i'm constantly surprised by the lessons i get from hearing how they problem-solve, despite how different our industries are

3. walk your own path - there will be noise. choose what you stand for, but be mindful of the big picture

Most people I spoke with this past week are still missing the "why now" in robotics.

In 2025, AI in robotics still looked like a research problem. In 2026 it is showing "sparks" of intelligence, similar to the moment right before ChatGPT was released.

Here is what changed. A year ago, NVIDIA was among the first to publish studies that ran on ~20K hours of robot training data and found scaling laws, similar to those in LLMs, between data scale and robot task performance. This year, Dyna Robotics (Dyna-2) took the idea further, pretrained models on 1M hours, and found that the scaling law holds at that scale.

Why scaling laws matter: robotics has conventionally been costly and integrator-heavy, because every new task and every new site was a new project. With pretraining and scaling laws, a model that adapts to a new task and a new site in hours changes how quickly robots can be put into the world to do useful work.

This is why every frontier lab is suddenly talking about "few-shot learning". First coined in the LLM space, it now means robots need only a few hours of demonstrations, sometimes as little as 2 hours, to perform useful tasks in sectors where tremendous labor shortages already exist.

The point was actually never to replace people. There is a long list of sectors that cannot hire fast enough: cleaning, data center construction and maintenance, solar. Robots let a strained workforce keep delivering the services we take for granted.

Scaling laws only pay off if the ecosystem can actually produce the next order of magnitude of data. 1M to 10M hours is orders of magnitude more challenging, and getting the industry to this next frontier of intelligence requires infrastructure that has never existed before.

One such bottleneck, and one that Hebbian actively works on, is quality control. AI models in robotics are more sensitive to noise than LLMs, which is why quality control is a core component that every AI model training pipeline relies heavily on.

Inspecting 1M hours of video for quality today costs $5.8M on Gemini 3-8 Flash. These models were never built for the demands of robotics QC, but they are the best option available to the data hyperscalers.

What ends up happening is that these data teams can only afford to run partial quality control, and buyers reluctantly accept such quality because there is no better alternative. This means the industry today is forgoing model improvement experiments for lack of high quality data, inhibiting progress in robotics.

Vendors themselves collect 1,500 hours to meet a 1,000-hour delivery. This works today. It does not survive 1M and 10M hours.

I have tremendous respect for the teams going against all odds to get us to the next frontier of robotics intelligence: @MeckaAI, @buildpbc, @ropedia_ai, Praxis, @DeepReach_AI, @DatoricAI, Luel and @joinHandshake on data.

And for the great teams at @cosmic_robotics, Proprio and Neuromorphic, pushing the frontier of what deployed robots can solve in the real world.

We think data quality tooling is the underinvested piece of infrastructure. Our mission is to accelerate the data engine for robots to learn from data at the scale general intelligence requires.

If you're excited to see more AI in robots, reach out.