# A3VLM: Actionable Articulation-Aware Vision Language Model

arXiv: https://arxiv.org/abs/2406.07549 · HTML: https://mlsanity.com/paper/2406.07549/

## Key facts

- Cited by 33 papers in the map (data through 14 Oct 2026).
- Placed in Embodied and 3D vision-language models, one of 16 directions in Multimodal LLMs / vision-language models.
- Builds on 36 of its 39 references that are in the map.
- First posted on arXiv 11 Jun 2024.

## Placed in

- Multimodal LLMs / vision-language models / [Embodied and 3D vision-language models](https://mlsanity.com/mllm/embodied-and-3d-vision-language-models/)

## Builds on (most cited first, 30 of 36)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Segment Anything](https://mlsanity.com/paper/2304.02643/) (2023)
- [DINOv2: Learning Robust Visual Features without Supervision](https://mlsanity.com/paper/2304.07193/) (2023)
- [Adding Conditional Control to Text-to-Image Diffusion Models](https://mlsanity.com/paper/2302.05543/) (2023)
- [Flamingo: a Visual Language Model for Few-Shot Learning](https://mlsanity.com/paper/2204.14198/) (2022)
- [RT-2: Vision-Language-Action Models Transfer Web Knowledge to Robotic Control](https://mlsanity.com/paper/2307.15818/) (2023)
- [Do As I Can, Not As I Say: Grounding Language in Robotic Affordances](https://mlsanity.com/paper/2204.01691/) (2022)
- [RT-1: Robotics Transformer for Real-World Control at Scale](https://mlsanity.com/paper/2212.06817/) (2022)
- [Code as Policies: Language Model Programs for Embodied Control](https://mlsanity.com/paper/2209.07753/) (2022)
- [Shikra: Unleashing Multimodal LLM's Referential Dialogue Magic](https://mlsanity.com/paper/2306.15195/) (2023)
- [VoxPoser: Composable 3D Value Maps for Robotic Manipulation with Language Models](https://mlsanity.com/paper/2307.05973/) (2023)
- [Point-BERT: Pre-training 3D Point Cloud Transformers with Masked Point Modeling](https://mlsanity.com/paper/2111.14819/) (2021)
- [SAPIEN: A SimulAted Part-based Interactive ENvironment](https://mlsanity.com/paper/2003.08515/) (2020)
- [PartNet: A Large-scale Benchmark for Fine-grained and Hierarchical Part-level 3D Object Understanding](https://mlsanity.com/paper/1812.02713/) (2018)
- [PointLLM: Empowering Large Language Models to Understand Point Clouds](https://mlsanity.com/paper/2308.16911/) (2023)
- [SPHINX: The Joint Mixing of Weights, Tasks, and Visual Embeddings for Multi-modal Large Language Models](https://mlsanity.com/paper/2311.07575/) (2023)
- [Scalable 3D Captioning with Pretrained Models](https://mlsanity.com/paper/2306.07279/) (2023)
- [Where2Act: From Pixels to Actions for Articulated 3D Objects](https://mlsanity.com/paper/2101.02692/) (2021)
- [RoboAgent: Generalization and Efficiency in Robot Manipulation via Semantic Augmentations and Action Chunking](https://mlsanity.com/paper/2309.01918/) (2023)
- [ManipLLM: Embodied Multimodal Large Language Model for Object-Centric Robotic Manipulation](https://mlsanity.com/paper/2312.16217/) (2023)
- [Category-Level Articulated Object Pose Estimation](https://mlsanity.com/paper/1912.11913/) (2019)
- [GAPartNet: Cross-Category Domain-Generalizable Object Perception and Manipulation via Generalizable and Actionable Parts](https://mlsanity.com/paper/2211.05272/) (2022)
- [ShapeLLM: Universal 3D Object Understanding for Embodied Interaction](https://mlsanity.com/paper/2402.17766/) (2024)
- [SPHINX-X: Scaling Data and Parameters for a Family of Multi-modal Large Language Models](https://mlsanity.com/paper/2402.05935/) (2024)
- [MOKA: Open-World Robotic Manipulation through Mark-Based Visual Prompting](https://mlsanity.com/paper/2403.03174/) (2024)
- [Shape2Motion: Joint Analysis of Motion Parts and Attributes from 3D Shapes](https://mlsanity.com/paper/1903.03911/) (2019)
- [Instruct2Act: Mapping Multi-modality Instructions to Robotic Actions with Large Language Model](https://mlsanity.com/paper/2305.11176/) (2023)
- [FlowBot3D: Learning 3D Articulation Flow to Manipulate Articulated Objects](https://mlsanity.com/paper/2205.04382/) (2022)
- [UMPNet: Universal Manipulation Policy Network for Articulated Objects](https://mlsanity.com/paper/2109.05668/) (2021)

## Built on by (most cited first, 30 of 33)

- [ReKep: Spatio-Temporal Reasoning of Relational Keypoint Constraints for Robotic Manipulation](https://mlsanity.com/paper/2409.01652/) (2024)
- [ShowUI: One Vision-Language-Action Model for GUI Visual Agent](https://mlsanity.com/paper/2411.17465/) (2024)
- [DreamVLA: A Vision-Language-Action Model Dreamed with Comprehensive World Knowledge](https://mlsanity.com/paper/2507.04447/) (2025)
- [Visual Prompting in Multimodal Large Language Models: A Survey](https://mlsanity.com/paper/2409.15310/) (2024)
- [SoFar: Language-Grounded Orientation Bridges Spatial Reasoning and Object Manipulation](https://mlsanity.com/paper/2502.13143/) (2025)
- [Embodied-R1: Reinforced Embodied Reasoning for General Robotic Manipulation](https://mlsanity.com/paper/2508.13998/) (2025)
- [ACoT-VLA: Action Chain-of-Thought for Vision-Language-Action Models](https://mlsanity.com/paper/2601.11404/) (2026)
- [CoA-VLA: Improving Vision-Language-Action Models via Visual-Textual Chain-of-Affordance](https://mlsanity.com/paper/2412.20451/) (2024)
- [PALM: Progress-Aware Policy Learning via Affordance Reasoning for Long-Horizon Robotic Manipulation](https://mlsanity.com/paper/2601.07060/) (2026)
- [Parallels Between VLA Model Post-Training and Human Motor Learning: Progress, Challenges, and Trends](https://mlsanity.com/paper/2506.20966/) (2025)
- [GLOVER: Generalizable Open-Vocabulary Affordance Reasoning for Task-Oriented Grasping](https://mlsanity.com/paper/2411.12286/) (2024)
- [Understanding Depth and Height Perception in Large Visual-Language Models](https://mlsanity.com/paper/2408.11748/) (2024)
- [UniAff: A Unified Representation of Affordances for Tool Usage and Articulation with Vision-Language Models](https://mlsanity.com/paper/2409.20551/) (2024)
- [CrayonRobo: Object-Centric Prompt-Driven Vision-Language-Action Model for Robotic Manipulation](https://mlsanity.com/paper/2505.02166/) (2025)
- [Focusing on What Matters: Object-Agent-centric Tokenization for Vision Language Action models](https://mlsanity.com/paper/2509.23655/) (2025)
- [Physically Ground Commonsense Knowledge for Articulated Object Manipulation with Analytic Concepts](https://mlsanity.com/paper/2503.23348/) (2025)
- [Neural Network Reprogrammability: A Unified Theme on Model Reprogramming, Prompt Tuning, and Prompt Instruction](https://mlsanity.com/paper/2506.04650/) (2025)
- [Embodied3DBench: Benchmarking Low-Level Embodied Spatial Intelligence of Vision Language Models](https://mlsanity.com/paper/2605.29074/) (2026)
- [Enhanced Vision-Language Models for Diverse Sensor Understanding: Cost-Efficient Optimization and Benchmarking](https://mlsanity.com/paper/2412.20750/) (2024)
- [ArtPro: Self-Supervised Articulated Object Reconstruction with Adaptive Integration of Mobility Proposals](https://mlsanity.com/paper/2602.22666/) (2026)
- [ArtMesh: Part-Aware Articulated Mesh Fields with Motion-Consistent Dynamics](https://mlsanity.com/paper/2605.16582/) (2026)
- [Action with Visual Primitives](https://mlsanity.com/paper/2605.22183/) (2026)
- [Analytic Concept-Centric Memory for Agentic Embodied Manipulation](https://mlsanity.com/paper/2606.29774/) (2026)
- [UnfoldArt: Zero-Shot Recovery of Full Articulated 3D Objects from Text or Image](https://mlsanity.com/paper/2606.30608/) (2026)
- [Flow-Enabled Generalization to Human Demonstrations in Few-Shot Imitation Learning](https://mlsanity.com/paper/2602.10594/) (2026)
- [Learning Structured Robot Policies from Vision-Language Models via Synthetic Neuro-Symbolic Supervision](https://mlsanity.com/paper/2604.02812/) (2026)
- [StateVLM: A State-Aware Vision-Language Model for Robotic Affordance Reasoning](https://mlsanity.com/paper/2605.03927/) (2026)
- [GSAM: A Generalizable and Safe Robotic Framework for Articulated Object Manipulation](https://mlsanity.com/paper/2605.30740/) (2026)
- [From Perception to Assistance: Open-Vocabulary Shared Autonomy for Robotic Manipulation](https://mlsanity.com/paper/2607.17323/) (2026)
- [ArtLang: Structured Language-to-Kinematics Grounding for Articulated 3D Actuation](https://mlsanity.com/paper/2608.15419/) (2026)

## Abstract (arXiv)

Vision Language Models (VLMs) have received significant attention in recent years in the robotics community. VLMs are shown to be able to perform complex visual reasoning and scene understanding tasks, which makes them regarded as a potential universal solution for general robotics problems such as manipulation and navigation. However, previous VLMs for robotics such as RT-1, RT-2, and ManipLLM have focused on directly learning robot-centric actions. Such approaches require collecting a significant amount of robot interaction data, which is extremely costly in the real world. Thus, we propose A3VLM, an object-centric, actionable, articulation-aware vision language model. A3VLM focuses on the articulation structure and action affordances of objects. Its representation is robot-agnostic and can be translated into robot actions using simple action primitives. Extensive experiments in both simulation benchmarks and real-world settings demonstrate the effectiveness and stability of A3VLM. We release our code and other materials at https://github.com/changhaonan/A3VLM.