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@andyzengineer (2026-08-04) says contact‑rich robot tasks force policies to model…

2026-08-04 · 15:36 UTC ·@andyzengineer ·1 min read

Brief

@andyzengineer (posted 2026-08-04) highlights that contact‑rich manipulation exposes the need for policies that generalize subtle friction and force behaviors to avoid wedging, noting the models' motion now feels more "human." GeneralistAI adds GEN‑1 yields 10–20× internal gains adapting to new actuators, boosting precision tasks like NIST‑board part disassembly.

Why it matters

@andyzengineer (2026-08-04) says contact‑rich robot tasks force policies to model fine‑grained friction and force nuances so objects don't get wedged or stuck, and he felt "goosebumps" seeing results that make robot motion seem more "human."

Key details

  • GeneralistAI reports GEN-1 achieves up to 10–20x gains on internal benchmarks for low‑level adaptation to new actuators and robots, substantially improving high‑precision tasks such as disassembling parts from a NIST board.
Source evidence

The models are getting better everyday.

This is a pretty contact-rich task, and forces us to reckon with the fact that our policies not only need to learn the quirks of different robots, but also have to do it in a way that generalizes to the fine-grained subtleties of friction and forces e.g. so that objects don’t get wedged or stuck.

Gave me goosebumps when I first saw these results internally. Something about it too, that just makes these models feel just a bit more “human” now in the way they move than they did before. Wild times.

Video

Generalist (@GeneralistAI)

We've improved how GEN-1 learns to adapt to new actuators and new robots at the lowest level, with up to 10-20x gains on internal benchmarks. This significantly boosts performance on high-precision tasks like disassembling parts from a NIST board.

Read more about GEN-1 in our blog posts in the comments below.

Video

— https://nitter.net/GeneralistAI/status/2084652475869774099#m