Samsung's RoboICL teaches frozen GPT-6 Astra robot skills via in-context demos, no training
机器之心 · wechat · 2026-10-01
Samsung's foundation model team (lead by Ye Hui Tang) released RoboICL: instead of training task-specific VLAs, it freezes GPT-6 Astra and feeds demonstrations plus the robot's own interaction history as embodied in-context data in a shared "observation → action → execution receipt → outcome" syntax, outputting dual-arm actions directly.
Key results
- 50.64 Overall across 30 RoboDojo bimanual tasks vs 39.56 for the strongest baseline Physical RSI; 20-27 points above the official zero-shot RoboProbe controller
- 54.75 zero-shot on Open tasks, showing that organizing online experience alone meaningfully changes a frozen model's control ability
- Project page publishes 929 playable three-viewpoint execution traces for layout-level inspection
- Real Franka arm: three tasks improve from 14.45 (0-shot) to 78.89 (3-shot)
Design insights
- Execution receipts pair proposed actions with actual outcomes so later decisions aren't built on wrong states
- Bounded anchored memory keeps initial interactions, timeline anchors and the latest interaction with explicit TRAJECTORYGAPs (80.25 vs 73.50 without)
- Budget ablations show balanced demo/memory slots (J=B=12) works best; demo-heavy (22,2) drops to 60.75
- 92.4% KV-cache hit rate on 122.5M input tokens thanks to stable prefixes
Limits: seed-0 sim runs, progress score ≠ success rate, only 3 real tasks with 5 trials each, and towel-folding tests reveal over-imitation of demo motion scale.
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