# A Probabilistic Framework for Learning Kinematic Models of Articulated Objects

arXiv: https://arxiv.org/abs/1405.7705 · HTML: https://mlsanity.com/paper/1405.7705/

## Key facts

- Cited by 40 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Jan 2014.

## Built on by (most cited first, 30 of 40)

- [Ditto: Building Digital Twins of Articulated Objects from Interaction](https://mlsanity.com/paper/2202.08227/) (2022)
- [VAT-Mart: Learning Visual Action Trajectory Proposals for Manipulating 3D ARTiculated Objects](https://mlsanity.com/paper/2106.14440/) (2021)
- [ArtGS: Building Interactable Replicas of Complex Articulated Objects via Gaussian Splatting](https://mlsanity.com/paper/2502.19459/) (2025)
- [CARTO: Category and Joint Agnostic Reconstruction of ARTiculated Objects](https://mlsanity.com/paper/2303.15782/) (2023)
- [CLA-NeRF: Category-Level Articulated Neural Radiance Field](https://mlsanity.com/paper/2202.00181/) (2022)
- [Act the Part: Learning Interaction Strategies for Articulated Object Part Discovery](https://mlsanity.com/paper/2105.01047/) (2021)
- [Ditto in the House: Building Articulation Models of Indoor Scenes through Interactive Perception](https://mlsanity.com/paper/2302.01295/) (2023)
- [Understanding 3D Object Articulation in Internet Videos](https://mlsanity.com/paper/2203.16531/) (2022)
- [Where2Explore: Few-shot Affordance Learning for Unseen Novel Categories of Articulated Objects](https://mlsanity.com/paper/2309.07473/) (2023)
- [Understanding 3D Object Interaction from a Single Image](https://mlsanity.com/paper/2305.09664/) (2023)
- [NAP: Neural 3D Articulation Prior](https://mlsanity.com/paper/2305.16315/) (2023)
- [Category-Independent Articulated Object Tracking with Factor Graphs](https://mlsanity.com/paper/2205.03721/) (2022)
- [Learning Environment-Aware Affordance for 3D Articulated Object Manipulation under Occlusions](https://mlsanity.com/paper/2309.07510/) (2023)
- [ArtFormer: Controllable Generation of Diverse 3D Articulated Objects](https://mlsanity.com/paper/2412.07237/) (2024)
- [Articulated Object Estimation in the Wild](https://mlsanity.com/paper/2509.01708/) (2025)
- [Distributional Depth-Based Estimation of Object Articulation Models](https://mlsanity.com/paper/2108.05875/) (2021)
- [VideoArtGS: Building Digital Twins of Articulated Objects from Monocular Video](https://mlsanity.com/paper/2509.17647/) (2025)
- [Unsupervised Kinematic Motion Detection for Part-segmented 3D Shape Collections](https://mlsanity.com/paper/2206.08497/) (2022)
- [Detection Based Part-level Articulated Object Reconstruction from Single RGBD Image](https://mlsanity.com/paper/2504.03177/) (2025)
- [SplArt: Articulation Estimation and Part-Level Reconstruction with 3D Gaussian Splatting](https://mlsanity.com/paper/2506.03594/) (2025)
- [Unsupervised Co-part Segmentation through Assembly](https://mlsanity.com/paper/2106.05897/) (2021)
- [CAPT: Category-level Articulation Estimation from a Single Point Cloud Using Transformer](https://mlsanity.com/paper/2402.17360/) (2024)
- [Articulated 3D Scene Graphs for Open-World Mobile Manipulation](https://mlsanity.com/paper/2602.16356/) (2026)
- [Learning to Infer Kinematic Hierarchies for Novel Object Instances](https://mlsanity.com/paper/2110.07911/) (2021)
- [Inferring Articulated Rigid Body Dynamics from RGBD Video](https://mlsanity.com/paper/2203.10488/) (2022)
- [Articulated 3D Human-Object Interactions from RGB Videos: An Empirical Analysis of Approaches and Challenges](https://mlsanity.com/paper/2209.05612/) (2022)
- [Pandora: Articulated 3D Scene Graphs from Egocentric Vision](https://mlsanity.com/paper/2603.28732/) (2026)
- [Learning Part Motion of Articulated Objects Using Spatially Continuous Neural Implicit Representations](https://mlsanity.com/paper/2311.12407/) (2023)
- [Structure from Action: Learning Interactions for Articulated Object 3D Structure Discovery](https://mlsanity.com/paper/2207.08997/) (2022)
- [Advances in 4D Representation: Geometry, Motion, and Interaction](https://mlsanity.com/paper/2510.19255/) (2025)

## Abstract (arXiv)

Robots operating in domestic environments generally need to interact with articulated objects, such as doors, cabinets, dishwashers or fridges. In this work, we present a novel, probabilistic framework for modeling articulated objects as kinematic graphs. Vertices in this graph correspond to object parts, while edges between them model their kinematic relationship. In particular, we present a set of parametric and non-parametric edge models and how they can robustly be estimated from noisy pose observations. We furthermore describe how to estimate the kinematic structure and how to use the learned kinematic models for pose prediction and for robotic manipulation tasks. We finally present how the learned models can be generalized to new and previously unseen objects. In various experiments using real robots with different camera systems as well as in simulation, we show that our approach is valid, accurate and efficient. Further, we demonstrate that our approach has a broad set of applications, in particular for the emerging fields of mobile manipulation and service robotics.