mlsanityData to 7 Oct 2026

My House, My Rules: Learning Tidying Preferences with Graph Neural Networks

arXiv 2111.03112 ↗First posted 4 Nov 2021cs.RO cs.LGcited by 27

Not on the map. Why?

Abstract (arXiv)

Robots that arrange household objects should do so according to the user's preferences, which are inherently subjective and difficult to model. We present NeatNet: a novel Variational Autoencoder architecture using Graph Neural Network layers, which can extract a low-dimensional latent preference vector from a user by observing how they arrange scenes. Given any set of objects, this vector can then be used to generate an arrangement which is tailored to that user's spatial preferences, with word embeddings used for generalisation to new objects. We develop a tidying simulator to gather rearrangement examples from 75 users, and demonstrate empirically that our method consistently produces neat and personalised arrangements across a variety of rearrangement scenarios.

Key facts. Cited by 27 papers in the map (data through 7 Oct 2026). Builds on 16 of its 31 references that are in the map. First posted on arXiv 4 Nov 2021.

Builds on

16 of 31 references.

References · 16

  1. 2014Adam: A Method for Stochastic Optimizationcited by 36,960
  2. 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,160
  3. 2017Graph Attention Networkscited by 8,854
  4. 2015You Only Look Once: Unified, Real-Time Object Detectioncited by 4,829
  5. 2013Auto-Encoding Variational Bayescited by 3,980
  6. 2019Fast Graph Representation Learning with PyTorch Geometriccited by 2,414
  7. 2016Enriching Word Vectors with Subword Informationcited by 1,578
  8. 2017Neural Collaborative Filteringcited by 1,108
  9. 2015Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs)cited by 1,061
  10. 2019DenseFusion: 6D Object Pose Estimation by Iterative Dense Fusioncited by 352
  11. 2020Integrated Task and Motion Planningcited by 281
  12. 2016Semantic Image Inpainting with Deep Generative Modelscited by 156
4 more
  1. 2020Rearrangement: A Challenge for Embodied AIcited by 153
  2. 2018Fusion++: Volumetric Object-Level SLAMcited by 97
  3. 2015Denoising Criterion for Variational Auto-Encoding Frameworkcited by 43
  4. 2020Autonomous Planning Based on Spatial Concepts to Tidy Up Home Environments with Service Robotscited by 6

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 8; 2023: 8; 2024: 4; 2025: 6; 2026: 1.

27 papers.

Sort:

By area:

  1. 2023TidyBot: Personalized Robot Assistance with Large Language Modelscited by 219
  2. 2022DALL-E-Bot: Introducing Web-Scale Diffusion Models to Roboticscited by 105
  3. 2022Housekeep: Tidying Virtual Households using Commonsense Reasoningcited by 57
  4. 2022IFOR: Iterative Flow Minimization for Robotic Object Rearrangementcited by 42
  5. 2024Instant Policy: In-Context Imitation Learning via Graph Diffusioncited by 38
  6. 2022Graph Neural Networks in IoT: A Surveycited by 27
  7. 2023Dream2Real: Zero-Shot 3D Object Rearrangement with Vision-Language Modelscited by 16
  8. 2026Learning Personalized Agents from Human Feedbackcited by 16
  9. 2022Transformers are Adaptable Task Plannerscited by 15
  10. 2025Embodied Agents Meet Personalization: Investigating Challenges and Solutions Through the Lens of Memory Utilizationcited by 15
  11. 2023Few-Shot In-Context Imitation Learning via Implicit Graph Alignmentcited by 14
  12. 2022TarGF: Learning Target Gradient Field to Rearrange Objects without Explicit Goal Specificationcited by 13
15 more
  1. 2024A Survey on Intelligent Internet of Things: Applications, Security, Privacy, and Future Directionscited by 10
  2. 2022Where To Start? Transferring Simple Skills to Complex Environmentscited by 9
  3. 2024"Set It Up!": Functional Object Arrangement with Compositional Generative Modelscited by 9
  4. 2023LVDiffusor: Distilling Functional Rearrangement Priors from Large Models into Diffusorcited by 7
  5. 2023Graph learning in robotics: a surveycited by 5
  6. 2025ADAPT: Actively Discovering and Adapting to Preferences for any Taskcited by 5
  7. 2023SceneScore: Learning a Cost Function for Object Arrangementcited by 4
  8. 2022Discovering Generalizable Spatial Goal Representations via Graph-based Active Reward Learningcited by 3
  9. 2024Robot Behavior Personalization from Sparse User Feedbackcited by 2
  10. 2023Effective Baselines for Multiple Object Rearrangement Planning in Partially Observable Mapped Environmentscited by 1
  11. 2025Tidiness Score-Guided Monte Carlo Tree Search for Visual Tabletop Rearrangementcited by 1
  12. 2025Personalized Robotic Object Rearrangement from Scene Contextcited by 1
  13. 2025GOPLA: Generalizable Object Placement Learning via Synthetic Augmentation of Human Arrangementcited by 1
  14. 2023Knolling Bot: Teaching Robots the Human Notion of Tidinesscited by 0
  15. 2025Explaining Why Things Go Where They Go: Interpretable Constructs of Human Organizational Preferencescited by 0