Robometer (Liang, Korkmaz, Zhang, Hwang et al.; arXiv 2603.02115) is a 4B reward model trained on a million trajectories. I tested it zero-shot as a judge on my own rollouts and on a 2 099-rollout public SO-101 benchmark. Episode length beat it on every held-out task. The paper never computes episode length.
SmolVLA (Shukor, Aubakirova, Capuano et al., Hugging Face; arXiv 2506.01844) is the policy behind almost every number on this site. Its paper's ablation tables are the manual for running it on a Jetson; three of them predicted things I later measured.
TurboVLA (Xie, Yao, Wu et al.; arXiv 2607.27205) drops the language model from the VLA and binds vision to text with Grounding-DINO-style cross-attention. My port failed on a two-task dataset, I concluded it ignored the instruction, then I found my port differed from the paper in four places, then I ran the paper's own trainer on my data. The faithful model ignores the instruction too. The data was the whole story.
VLA-Corrector (Song, Qian, Feng, Song; arXiv 2609.06508) wraps a frozen π0.5 in a tiny learned verifier that routes a recovery prompt. I borrowed one of its signals — cosine drift of the latent — as a monitor. It caught nothing. The part of the paper I should have borrowed is the verifier's design.
VLA-JEPA (Sun, Zhang, Qi et al.; arXiv 2602.10098) pretrains a VLA on human video through a latent world model. I trained it for $10, ran it on the arm at 9/10, and tested whether its eight samples were worth choosing between. The paper's honest ablation row says what to expect: robustness, not accuracy.
Three weeks of gates reduced the world-model stack to a verifier and a shadow signal. What remained was the actual bottleneck of a one-person robotics lab: deciding what to record next, running trials unattended, and keeping the training data clean. v1.5 is that loop, specified from nine recent papers on agent harnesses and self-improvement, and partly built. It has not run end to end on the arm yet; this post is a design note, not a result.
On 2026-10-18 two Jetson gates went into the register with bars frozen beforehand: G11-A passed, G12 failed. Both were voided the same day. G11-A timed the right model with the wrong weights; G12 timed the wrong model. Neither was a training or hardware problem. Both were caught by checking what the script had actually loaded.
The deterministic judge is right but narrow: it knows about two coloured bowls. Any new task needs a judge that reads video. NVIDIA's Flora-Rewarder-27B, a robot-trained outcome scorer, passed where Gemini had failed the day before. The ablation says why, and it is not the robot training.
One Jetson day, no arm motion, every mid-episode verifier in the v1.0 design run against 50 fresh rollouts with the bars frozen the night before. Stage trackers, VLM judges and the world-model reflex all failed. One offline engine experiment passed. The architecture that came out of it (v1.1) verifies only at rest, and that rule has not moved since.
With a judge that is right at rest, the world model finally had a fair question: can it read the judge's verdict from the latent, and can it see it coming? G10 ran on the Mac for free. It is the one world-model gate that passed cleanly, and the next post is about how much of it survived contact with fresh data.