In-context Robot Learning Made Simple: A Democratized Recipe for Manipulation Tasks

arXiv:2609.38173 · cs.RO · Submitted 2026-09-29 · Read on arXiv

cs.RO

Submitted: 2026-09-29

Updated: 2026-10-08

Project page: https://simpleicl.github.io/simpleicl

Terminology

• Affordance — 24× in this paper · explained in 1 episode
• Generalization — 9× in this paper · explained in 13 episodes
• In-Context Learning — 8× in this paper · explained in 4 episodes
• World Model — 7× in this paper · explained in 7 episodes
• Data Augmentation — 5× in this paper · explained in 7 episodes
• Embeddings — 5× in this paper · explained in 2 episodes
• Robustness — 5× in this paper · explained in 5 episodes
• Foundation Model — 4× in this paper · explained in 3 episodes
• In-Context Robot Learning — 3× in this paper
• Adaptation — 2× in this paper · explained in 1 episode
• Core — 2× in this paper · explained in 2 episodes
• Foundation Models — 2× in this paper · explained in 7 episodes
• Grounding — 2× in this paper · explained in 3 episodes
• Latent Space — 2× in this paper · explained in 7 episodes
• Zero-Shot Generalization — 2× in this paper · explained in 5 episodes
• Action Flow — 1× in this paper · explained in 1 episode
• Action Primitives — 1× in this paper · explained in 1 episode
• Benchmark — 1× in this paper · explained in 2 episodes
• Benchmarking — 1× in this paper · explained in 4 episodes
• Consistency — 1× in this paper · explained in 3 episodes
• Diffusion Model — 1× in this paper · explained in 8 episodes
• Fine-Tuning — 1× in this paper · explained in 5 episodes
• Generative Model — 1× in this paper · explained in 2 episodes
• Generative Models — 1× in this paper · explained in 3 episodes
• In-Context Learning (ICL) — 1× in this paper · explained in 5 episodes
• Large Language Models (LLMs) — 1× in this paper · explained in 6 episodes
• Reinforcement Learning — 1× in this paper · explained in 4 episodes
• Reinforcement Learning (RL) — 1× in this paper · explained in 25 episodes
• Steering — 1× in this paper · explained in 2 episodes

Sources

• Gen2Act: Human Video Generation in Novel Scenarios enables Generalizable Robot Manipulation
• Motus: A Unified Latent Action World Model
• GR00T N1: An Open Foundation Model for Generalist Humanoid Robots
• $\pi_0$: A Vision-Language-Action Flow Model for General Robot Control
• AgiBot World Colosseo: A Large-scale Manipulation Platform for Scalable and Intelligent Embodied Systems
• RynnVLA-002: A Unified Vision-Language-Action and World Model
• GR-2: A Generative Video-Language-Action Model with Web-Scale Knowledge for Robot Manipulation
• Robots Acquire Manipulation Skills in Seconds from a Single Human Video
• RoboTwin 2.0: A Scalable Data Generator and Benchmark with Strong Domain Randomization for Robust Bimanual Robotic Manipulation
• Learning Universal Policies via Text-Guided Video Generation
• Vidar: Embodied Video Diffusion Model for Generalist Manipulation
• WAM-TTT: Steering World-Action Models by Watching Human Play at Test Time
• $\pi_{0.5}$: a Vision-Language-Action Model with Open-World Generalization
• DreamGen: Unlocking Generalization in Robot Learning through Video World Models
• OpenVLA: An Open-Source Vision-Language-Action Model
• Cosmos Policy: Fine-Tuning Video Models for Visuomotor Control and Planning
• Causal World Modeling for Robot Control
• Video Generators are Robot Policies
• Genie Envisioner: A Unified World Foundation Platform for Robotic Manipulation
• RDT-1B: a Diffusion Foundation Model for Bimanual Manipulation

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