# Where2Explore: Few-shot Affordance Learning for Unseen Novel Categories of Articulated Objects

arXiv: https://arxiv.org/abs/2309.07473 · HTML: https://mlsanity.com/paper/2309.07473/

## Key facts

- Cited by 35 papers in the map (data through 14 Oct 2026).
- Builds on 37 of its 48 references that are in the map.
- First posted on arXiv 14 Sep 2023.

## Builds on (most cited first, 30 of 37)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space](https://mlsanity.com/paper/1706.02413/) (2017)
- [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)
- [Where2Act: From Pixels to Actions for Articulated 3D Objects](https://mlsanity.com/paper/2101.02692/) (2021)
- [Category-Level Articulated Object Pose Estimation](https://mlsanity.com/paper/1912.11913/) (2019)
- [Ditto: Building Digital Twins of Articulated Objects from Interaction](https://mlsanity.com/paper/2202.08227/) (2022)
- [GAPartNet: Cross-Category Domain-Generalizable Object Perception and Manipulation via Generalizable and Actionable Parts](https://mlsanity.com/paper/2211.05272/) (2022)
- [Shape2Motion: Joint Analysis of Motion Parts and Attributes from 3D Shapes](https://mlsanity.com/paper/1903.03911/) (2019)
- [A-SDF: Learning Disentangled Signed Distance Functions for Articulated Shape Representation](https://mlsanity.com/paper/2104.07645/) (2021)
- [Synergies Between Affordance and Geometry: 6-DoF Grasp Detection via Implicit Representations](https://mlsanity.com/paper/2104.01542/) (2021)
- [3D AffordanceNet: A Benchmark for Visual Object Affordance Understanding](https://mlsanity.com/paper/2103.16397/) (2021)
- [FlowBot3D: Learning 3D Articulation Flow to Manipulate Articulated Objects](https://mlsanity.com/paper/2205.04382/) (2022)
- [Learning Dexterous Grasping with Object-Centric Visual Affordances](https://mlsanity.com/paper/2009.01439/) (2020)
- [CAPTRA: CAtegory-level Pose Tracking for Rigid and Articulated Objects from Point Clouds](https://mlsanity.com/paper/2104.03437/) (2021)
- [VAT-Mart: Learning Visual Action Trajectory Proposals for Manipulating 3D ARTiculated Objects](https://mlsanity.com/paper/2106.14440/) (2021)
- [Deep Part Induction from Articulated Object Pairs](https://mlsanity.com/paper/1809.07417/) (2018)
- [DexArt: Benchmarking Generalizable Dexterous Manipulation with Articulated Objects](https://mlsanity.com/paper/2305.05706/) (2023)
- [UMPNet: Universal Manipulation Policy Network for Articulated Objects](https://mlsanity.com/paper/2109.05668/) (2021)
- [CARTO: Category and Joint Agnostic Reconstruction of ARTiculated Objects](https://mlsanity.com/paper/2303.15782/) (2023)
- [Grounding 3D Object Affordance from 2D Interactions in Images](https://mlsanity.com/paper/2303.10437/) (2023)
- [AdaAfford: Learning to Adapt Manipulation Affordance for 3D Articulated Objects via Few-shot Interactions](https://mlsanity.com/paper/2112.00246/) (2021)
- [End-to-End Affordance Learning for Robotic Manipulation](https://mlsanity.com/paper/2209.12941/) (2022)
- [OPD: Single-view 3D Openable Part Detection](https://mlsanity.com/paper/2203.16421/) (2022)
- [Act the Part: Learning Interaction Strategies for Articulated Object Part Discovery](https://mlsanity.com/paper/2105.01047/) (2021)
- [Articulated Object Interaction in Unknown Scenes with Whole-Body Mobile Manipulation](https://mlsanity.com/paper/2103.10534/) (2021)
- [Ditto in the House: Building Articulation Models of Indoor Scenes through Interactive Perception](https://mlsanity.com/paper/2302.01295/) (2023)
- [A Probabilistic Framework for Learning Kinematic Models of Articulated Objects](https://mlsanity.com/paper/1405.7705/) (2014)
- [Visual Identification of Articulated Object Parts](https://mlsanity.com/paper/2012.00284/) (2020)

## Built on by (most cited first, 30 of 35)

- [Robo-ABC: Affordance Generalization Beyond Categories via Semantic Correspondence for Robot Manipulation](https://mlsanity.com/paper/2401.07487/) (2024)
- [VidBot: Learning Generalizable 3D Actions from In-the-Wild 2D Human Videos for Zero-Shot Robotic Manipulation](https://mlsanity.com/paper/2503.07135/) (2025)
- [A Survey of Embodied Learning for Object-Centric Robotic Manipulation](https://mlsanity.com/paper/2408.11537/) (2024)
- [UniGarmentManip: A Unified Framework for Category-Level Garment Manipulation via Dense Visual Correspondence](https://mlsanity.com/paper/2405.06903/) (2024)
- [FunGraph: Functionality Aware 3D Scene Graphs for Language-Prompted Scene Interaction](https://mlsanity.com/paper/2503.07909/) (2025)
- [Learning 2D Invariant Affordance Knowledge for 3D Affordance Grounding](https://mlsanity.com/paper/2408.13024/) (2024)
- [Survey on Modeling of Human-made Articulated Objects](https://mlsanity.com/paper/2403.14937/) (2024)
- [GLOVER++: Unleashing the Potential of Affordance Learning from Human Behaviors for Robotic Manipulation](https://mlsanity.com/paper/2505.11865/) (2025)
- [Open-World Reinforcement Learning over Long Short-Term Imagination](https://mlsanity.com/paper/2410.03618/) (2024)
- [AdaManip: Adaptive Articulated Object Manipulation Environments and Policy Learning](https://mlsanity.com/paper/2502.11124/) (2025)
- [AffordanceVLA: A Vision-Language-Action Model Empowering Action Generation through Affordance-Aware Understanding](https://mlsanity.com/paper/2606.06155/) (2026)
- [PreAfford: Universal Affordance-Based Pre-Grasping for Diverse Objects and Environments](https://mlsanity.com/paper/2404.03634/) (2024)
- [From Screens to Scenes: A Survey of Embodied AI in Healthcare](https://mlsanity.com/paper/2501.07468/) (2025)
- [Seeing the Unseen: Visual Common Sense for Semantic Placement](https://mlsanity.com/paper/2401.07770/) (2024)
- [GarmentPile: Point-Level Visual Affordance Guided Retrieval and Adaptation for Cluttered Garments Manipulation](https://mlsanity.com/paper/2503.09243/) (2025)
- [EqvAfford: SE(3) Equivariance for Point-Level Affordance Learning](https://mlsanity.com/paper/2408.01953/) (2024)
- [ConceptFactory: Facilitate 3D Object Knowledge Annotation with Object Conceptualization](https://mlsanity.com/paper/2411.00448/) (2024)
- [Arti-PG: A Toolbox for Procedurally Synthesizing Large-Scale and Diverse Articulated Objects with Rich Annotations](https://mlsanity.com/paper/2412.14974/) (2024)
- [Learning Part Motion of Articulated Objects Using Spatially Continuous Neural Implicit Representations](https://mlsanity.com/paper/2311.12407/) (2023)
- [Physically Ground Commonsense Knowledge for Articulated Object Manipulation with Analytic Concepts](https://mlsanity.com/paper/2503.23348/) (2025)
- [BiAssemble: Learning Collaborative Affordance for Bimanual Geometric Assembly](https://mlsanity.com/paper/2506.06221/) (2025)
- [Adaptive Articulated Object Manipulation On The Fly with Foundation Model Reasoning and Part Grounding](https://mlsanity.com/paper/2507.18276/) (2025)
- [Executable Analytic Concepts as the Missing Link Between VLM Insight and Precise Manipulation](https://mlsanity.com/paper/2510.07975/) (2025)
- [Visual-Geometric Collaborative Guidance for Affordance Learning](https://mlsanity.com/paper/2410.11363/) (2024)
- [Opening Articulated Structures in the Real World](https://mlsanity.com/paper/2402.17767/) (2024)
- [Object Affordance Recognition and Grounding via Multi-scale Cross-modal Representation Learning](https://mlsanity.com/paper/2508.01184/) (2025)
- [QDTraj: Exploration of Diverse Trajectory Primitives for Articulated Objects Robotic Manipulation](https://mlsanity.com/paper/2604.22551/) (2026)
- [Watch Less, Feel More: Sim-to-Real RL for Generalizable Articulated Object Manipulation via Motion Adaptation and Impedance Control](https://mlsanity.com/paper/2502.14457/) (2025)
- [From Scan to Action: Leveraging Realistic Scans for Embodied Scene Understanding](https://mlsanity.com/paper/2507.17585/) (2025)
- [ASkDAgger: Active Skill-level Data Aggregation for Interactive Imitation Learning](https://mlsanity.com/paper/2508.05310/) (2025)

## Abstract (arXiv)

Articulated object manipulation is a fundamental yet challenging task in robotics. Due to significant geometric and semantic variations across object categories, previous manipulation models struggle to generalize to novel categories. Few-shot learning is a promising solution for alleviating this issue by allowing robots to perform a few interactions with unseen objects. However, extant approaches often necessitate costly and inefficient test-time interactions with each unseen instance. Recognizing this limitation, we observe that despite their distinct shapes, different categories often share similar local geometries essential for manipulation, such as pullable handles and graspable edges - a factor typically underutilized in previous few-shot learning works. To harness this commonality, we introduce 'Where2Explore', an affordance learning framework that effectively explores novel categories with minimal interactions on a limited number of instances. Our framework explicitly estimates the geometric similarity across different categories, identifying local areas that differ from shapes in the training categories for efficient exploration while concurrently transferring affordance knowledge to similar parts of the objects. Extensive experiments in simulated and real-world environments demonstrate our framework's capacity for efficient few-shot exploration and generalization.