RoboICL: Embodied In-Context Learning with GPT-6 Astra

arXiv:2609.34261 · cs.RO, cs.LG · Submitted 2026-09-28 · Read on arXiv

cs.RO, cs.LG

Submitted: 2026-09-28

Updated: 2026-09-28

Code: https://github.com/Mosi-AI/RoboICL

Terminology

• Benchmark — 7× in this paper · explained in 2 episodes
• In-Context Learning — 5× in this paper · explained in 4 episodes
• Adaptation — 4× in this paper · explained in 1 episode
• Alignment — 3× in this paper · explained in 1 episode
• Foundation Model — 3× in this paper · explained in 3 episodes
• Vision-Language Model — 3× in this paper
• Calibration — 2× in this paper · explained in 7 episodes
• Few-Shot Learning — 2× in this paper · explained in 4 episodes
• Frozen model — 2× in this paper · explained in 2 episodes
• Trace — 2× in this paper · explained in 2 episodes
• Action Space — 1× in this paper · explained in 1 episode
• Adaptability — 1× in this paper · explained in 1 episode
• Affordance — 1× in this paper · explained in 1 episode
• Failure Modes — 1× in this paper · explained in 3 episodes
• Foundation Models — 1× in this paper · explained in 7 episodes
• Generalization — 1× in this paper · explained in 13 episodes
• Grounding — 1× in this paper · explained in 3 episodes
• Hybrid Approach — 1× in this paper · explained in 4 episodes
• Large Language Models (LLMs) — 1× in this paper · explained in 6 episodes
• Many Vision-Language-Action — 1× in this paper
• Multimodal Inputs — 1× in this paper · explained in 2 episodes
• Provenance — 1× in this paper · explained in 3 episodes
• Pruning — 1× in this paper · explained in 4 episodes
• Reinforcement Learning — 1× in this paper · explained in 4 episodes
• Robustness — 1× in this paper · explained in 5 episodes
• Verification — 1× in this paper · explained in 2 episodes
• Vision-Language Model (VLM) — 1× in this paper · explained in 3 episodes

Sources

• Do As I Can, Not As I Say: Grounding Language in Robotic Affordances
• Flamingo: a Visual Language Model for Few-Shot Learning
• Zeva: In-Context Causal Learning for Generalizable Embodied Manipulation
• RoboDojo: A Unified Sim-and-Real Benchmark for Comprehensive Evaluation of Generalist Robot Manipulation Policies
• Show-Harness: Just a VLM Agent Can Play Robots
• In-Context Robot Learning with VLM Agents
• In-Context Imitation Learning via Next-Token Prediction
• Reflective VLA: In-Context Action Consequences Make VLAs Generalize
• Code as Policies: Language Model Programs for Embodied Control
• G0.5: One Autoregressive Stream for Robot Reasoning and Action
• Reflexion: Language Agents with Verbal Reinforcement Learning
• ReAct: Synergizing Reasoning and Acting in Language Models
• In-Context Learning Enables Robot Action Prediction in LLMs

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