RoboICL: teaching robots new tasks via in-context learning instead of retraining
青稞AI · wechat · 2026-09-19
RoboICL explores letting robots learn new tasks on the fly from demonstrations in context, without retraining: show the robot examples → it infers task structure → executes directly. In a sequence-imitation task, the robot observes a series of operations, grasps spatial relations and ordering, then reproduces the process in a new scene.
Combined with LLMs, the large model handles vision → task understanding → action planning → execution → environment feedback, moving robotics from memorizing training data to context-adaptive behavior. The post suggests this could shift training from "one model per task" toward "one general model for many tasks." Blog and GitHub links in the original.
More from Embodied
- GAVEL harness lifts Qwen3-8B from 41.2% to 91.8% on long-horizon robot tasks — dair_ai · 2026-09-20
- Musk confirms every Starlink V3 satellite will carry an Nvidia Vera Rubin NVL72 — 100k sats, 25GW — ns123abc · 2026-09-20
- Odyssey-3: One Pretrained World Model Adapts to Different Robot Arms With Hours of Demos — ChongZzZhang · 2026-09-20
- iPhone 18 Pro motherboard weighs just 13.1g yet packs PC-class compute — SumitGup · 2026-09-20
- How did Apple Silicon get 50% faster in three years? – Daniel Lemire — ibobev · 2026-09-20
- Snap teams with Salesforce, Amazon and Nvidia to push Specs glasses into the enterprise — matt_slotnick · 2026-09-20