While GPT-6-Astra is making everyone anxious, we’ve been working on the System 1 side of robotics: timely responses, continuous motion, and efficient execution. I think a robot’s intelligence also shows in its ability to keep pace with a changing world. Perception, inference, and control operate at different rates and with different latencies. New observations keep arriving, inference takes time, and actions need to keep moving forward. Coordinating these asynchronous streams—so a robot can act smoothly despite latency and adjust as new information arrives—is a problem worth studying carefully. Real-time performance takes work across the whole system. Model capabilities, inference strategies, and execution mechanisms have to come together in a continuous feedback loop. I’d love to see more real-robot experiments and discussion around these questions. Proud of our team for putting over two months of work into ManiMux, now open source. I hope these tools make it easier for more people to deploy their robots and test their ideas. Give it a try—contributions welcome!

I finally baked a long-form audit of “robot scaling laws.” AI agents did a lot of the digging; I did a lot of checking, rewriting, and arguing with the drafts. I reviewed 19 public results related to robot scaling. The short version: scaling curve ✅ scaling law ❌ Here is the evidence funnel: 19 public results reviewed → 16 report multi-scale curves → 13 keep the main variable and evaluation reasonably controlled → 1 tests a held-out loss forecast → 0 completes the full loop from fitting loss, to forecasting the next scale, to validating that forecast on real robots from the same model family. This distinction matters. In LLM scaling experiments, data volume D is usually measured in tokens. Token count does not tell us how much new information the data contains, but it remains a relatively stable training unit when the tokenizer, data pipeline, and distribution are held fixed. Robotics does not yet have an equivalent common unit. Different studies use recorded hours, trajectories, frames, action steps, or action tokens. Even the same “one hour” can produce very different training examples under different sampling rates, action representations, chunking schemes, filtering rules, tasks, environments, and embodiments. The reported objectives and y-axes vary even further: held-out denoising or vector-field MSE, decoded action error, thresholded accuracy, simulated progress, task completion, cycle time, and real-robot success all answer different questions. A few examples: — Figure Helix 2.5 provides the only held-out loss forecast I found: the first three Index-data scales are used to predict offline action loss at 8×. But its 9%→56% real-robot result comes from a separate with-vs-without-pretraining experiment, not the endpoints of that loss curve. — Dyna-2 scales egocentric human video from 1k to 1M hours. Offline [email protected] moves from roughly 40% to 47%, while post-trained robot performance rises from 20% to 53% on its normalized metric. Scaling helps, but the public results cannot tell us whether the final 900k hours were worth the cost. — Xiaomi-Robotics-1 reports that, with a fixed 5B model, increasing UMI data from roughly 2.5k to 20k hours raises mean success from 53% to 75%. At fixed data, moving from 5B to 10B adds only four points, from 75% to 79%. Useful evidence, but the full model × data grid is still missing. — Sunday ACT-2 shows that a quality-selected 12.5% subset can outperform a uniformly sampled 50% subset. Nominal scale is not enough: data distribution, repetition, and coverage can matter more than another hour of collection. My current view: A general law spanning tasks, environments, and embodiments will probably arrive later, because robotics still lacks a shared training and validation distribution. But deployment may not need to wait for a predictive scaling law. If a system is reliable enough on a concrete task at an acceptable cost, products may arrive before a general law does. Perhaps robotics is not waiting for a GPT moment, but an iPhone moment: hardware, software, data, and intelligence finally coming together in a product that works reliably in the real world. Full visual audit, original figures, evidence boundaries, and sources: minghuanliu.com/robot_scalin…