Transferring the Intelligence of VLMs to Robotic Control

arXiv:2609.22966 · cs.RO · Submitted 2026-09-19 · Read on arXiv

cs.RO

Submitted: 2026-09-19

Updated: 2026-09-19

Terminology

• Benchmark — 13× in this paper · explained in 2 episodes
• Generalization — 7× in this paper · explained in 13 episodes
• Foundation Model — 5× in this paper · explained in 3 episodes
• Adaptation — 4× in this paper · explained in 1 episode
• Grounding — 4× in this paper · explained in 3 episodes
• Ablation — 3× in this paper · explained in 1 episode
• Transformer — 3× in this paper · explained in 2 episodes
• Ablation Studies — 2× in this paper · explained in 1 episode
• Foundation Models — 2× in this paper · explained in 7 episodes
• In-Context Learning — 2× in this paper · explained in 4 episodes
• Instruction Tuning — 2× in this paper · explained in 2 episodes
• Motion Primitives — 2× in this paper · explained in 2 episodes
• Test-time Scaling — 2× in this paper · explained in 2 episodes
• Ablation study — 1× in this paper · explained in 1 episode
• Abstraction — 1× in this paper · explained in 1 episode
• Action Space — 1× in this paper · explained in 1 episode
• Adaptive Planning — 1× in this paper · explained in 1 episode
• Affordance — 1× in this paper · explained in 1 episode
• Alignment — 1× in this paper · explained in 1 episode
• Benchmarking — 1× in this paper · explained in 3 episodes
• Catastrophic Forgetting — 1× in this paper · explained in 5 episodes
• Failure Modes — 1× in this paper · explained in 3 episodes
• Fine-Tuning — 1× in this paper · explained in 5 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
• Multimodal Reasoning — 1× in this paper · explained in 5 episodes
• Steering — 1× in this paper · explained in 2 episodes
• VLA Models — 1× in this paper · explained in 3 episodes
• Verification — 1× in this paper · explained in 2 episodes
• Vision-Language Models (VLMs) — 1× in this paper · explained in 6 episodes
• Vision-Language-Action (VLA) Models — 1× in this paper · explained in 2 episodes
• World Model — 1× in this paper · explained in 7 episodes

Sources

• Neural Machine Translation by Jointly Learning to Align and Translate
• GR00T N1: An Open Foundation Model for Generalist Humanoid Robots
• $\pi_0$: A Vision-Language-Action Flow Model for General Robot Control
• Xiaomi-Robotics-0: An Open-Sourced Vision-Language-Action Model with Real-Time Execution
• Evaluating Large Language Models Trained on Code
• RoboTwin 2.0: A Scalable Data Generator and Benchmark with Strong Domain Randomization for Robust Bimanual Robotic 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
• StarVLA: A Lego-like Codebase for Vision-Language-Action Model Developing
• CaP-X: A Framework for Benchmarking and Improving Coding Agents for Robot Manipulation
• Unified 4D World Action Modeling from Video Priors with Asynchronous Denoising
• $\pi_{0.5}$: a Vision-Language-Action Model with Open-World Generalization
• Galaxea Open-World Dataset and G0 Dual-System VLA Model
• G0.5: One Autoregressive Stream for Robot Reasoning and Action
• Goal2Skill: Long-Horizon Manipulation with Adaptive Planning and Reflection
• Very Deep Convolutional Networks for Large-Scale Image Recognition
• Gemini Robotics: Bringing AI into the Physical World
• Motubrain: An Advanced World Action Model for Robot Control
• A Pragmatic VLA Foundation Model
• Agentic Robot: A Brain-Inspired Framework for Vision-Language-Action Models in Embodied Agents

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