TidyBot: Personalized Robot Assistance with Large Language Models
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Cited by 26 in Multimodal LLMs / vision-language models, but placed by its own abstract, which didn’t fit.
Abstract (arXiv)
For a robot to personalize physical assistance effectively, it must learn user preferences that can be generally reapplied to future scenarios. In this work, we investigate personalization of household cleanup with robots that can tidy up rooms by picking up objects and putting them away. A key challenge is determining the proper place to put each object, as people's preferences can vary greatly depending on personal taste or cultural background. For instance, one person may prefer storing shirts in the drawer, while another may prefer them on the shelf. We aim to build systems that can learn such preferences from just a handful of examples via prior interactions with a particular person. We show that robots can combine language-based planning and perception with the few-shot summarization capabilities of large language models (LLMs) to infer generalized user preferences that are broadly applicable to future interactions. This approach enables fast adaptation and achieves 91.2% accuracy on unseen objects in our benchmark dataset. We also demonstrate our approach on a real-world mobile manipulator called TidyBot, which successfully puts away 85.0% of objects in real-world test scenarios.
Key facts. Cited by 219 papers in the map (data through 7 Oct 2026). Builds on 48 of its 76 references that are in the map. First posted on arXiv 9 May 2023.
Builds on
48 of 76 references.
References · 48
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- 2021iGibson 2.0: Object-Centric Simulation for Robot Learning of Everyday Household Taskscited by 191
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- 2017Robotic Pick-and-Place of Novel Objects in Clutter with Multi-Affordance Grasping and Cross-Domain Image Matchingcited by 124
- 2021Visual Room Rearrangementcited by 101
- 2021ManipulaTHOR: A Framework for Visual Object Manipulationcited by 101
- 2022Grounding Language with Visual Affordances over Unstructured Datacited by 101
- 2021The ThreeDWorld Transport Challenge: A Visually Guided Task-and-Motion Planning Benchmark for Physically Realistic Embodied AIcited by 64
- 2022Housekeep: Tidying Virtual Households using Commonsense Reasoningcited by 57
- 2022TIDEE: Tidying Up Novel Rooms using Visuo-Semantic Commonsense Priorscited by 30
- 2021My House, My Rules: Learning Tidying Preferences with Graph Neural Networkscited by 27
- 2022Leveraging Language for Accelerated Learning of Tool Manipulationcited by 26
- 2021Leveraging the Inductive Bias of Large Language Models for Abstract Textual Reasoningcited by 18
- 2019Multi-Object Rearrangement with Monte Carlo Tree Search:A Case Study on Planar Nonprehensile Sortingcited by 12
- 2022Leveraging Large (Visual) Language Models for Robot 3D Scene Understandingcited by 10
- 2020Autonomous Planning Based on Spatial Concepts to Tidy Up Home Environments with Service Robotscited by 6
- 2020Decision Making in Joint Push-Grasp Action Space for Large-Scale Object Sortingcited by 5
- 2016Classifying and sorting cluttered piles of unknown objects with robots: a learning approachcited by 2
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Citing papers per year: 2023: 56; 2024: 86; 2025: 47; 2026: 30.
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