mlsanityData to 7 Oct 2026

Affordances in Robotic Tasks -- A Survey

arXiv 2004.07400 ↗First posted 15 Apr 2020cs.ROcited by 28

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Abstract (arXiv)

Affordances are key attributes of what must be perceived by an autonomous robotic agent in order to effectively interact with novel objects. Historically, the concept derives from the literature in psychology and cognitive science, where affordances are discussed in a way that makes it hard for the definition to be directly transferred to computational specifications useful for robots. This review article is focused specifically on robotics, so we discuss the related literature from this perspective. In this survey, we classify the literature and try to find common ground amongst different approaches with a view to application in robotics. We propose a categorisation based on the level of prior knowledge that is assumed to build the relationship among different affordance components that matter for a particular robotic task. We also identify areas for future improvement and discuss possible directions that are likely to be fruitful in terms of impact on robotics practice.

Key facts. Cited by 28 papers in the map (data through 7 Oct 2026). First posted on arXiv 15 Apr 2020.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 4; 2022: 1; 2023: 5; 2024: 12; 2025: 3; 2026: 3.

28 papers.

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  1. 2023LOCATE: Localize and Transfer Object Parts for Weakly Supervised Affordance Groundingcited by 64
  2. 2021Semantics for Robotic Mapping, Perception and Interaction: A Surveycited by 56
  3. 2023AffordPose: A Large-scale Dataset of Hand-Object Interactions with Affordance-driven Hand Posecited by 47
  4. 2023One-Shot Open Affordance Learning with Foundation Modelscited by 45
  5. 2024RT-Affordance: Affordances are Versatile Intermediate Representations for Robot Manipulationcited by 40
  6. 2024AffordDP: Generalizable Diffusion Policy with Transferable Affordancecited by 35
  7. 2024The Essential Role of Causality in Foundation World Models for Embodied AIcited by 32
  8. 2024One-Shot Imitation Learning with Invariance Matching for Robotic Manipulationcited by 18
  9. 2023Learning 6-DoF Fine-grained Grasp Detection Based on Part Affordance Groundingcited by 17
  10. 2024INTRA: Interaction Relationship-aware Weakly Supervised Affordance Groundingcited by 12
  11. 2024What does CLIP know about peeling a banana?cited by 9
  12. 2023SSR-2D: Semantic 3D Scene Reconstruction from 2D Imagescited by 7
16 more
  1. 2021Are standard Object Segmentation models sufficient for Learning Affordance Segmentation?cited by 6
  2. 2021Navigation-Oriented Scene Understanding for Robotic Autonomy: Learning to Segment Driveability in Egocentric Imagescited by 6
  3. 2024Egocentric and Exocentric Methods: A Short Surveycited by 6
  4. 2025AffordanceSAM: Segment Anything Once More in Affordance Groundingcited by 6
  5. 2025Affogato: Open-Vocabulary Affordance Grounding with Automated Data Generation at Scalecited by 6
  6. 2024Self-Explainable Affordance Learning with Embodied Captioncited by 4
  7. 2024PAVLM: Advancing Point Cloud based Affordance Understanding Via Vision-Language Modelcited by 4
  8. 2022Recognising Affordances in Predicted Futures to Plan with Consideration of Non-canonical Affordance Effectscited by 1
  9. 2025Visual Affordance Prediction: Survey and Reproducibilitycited by 1
  10. 2026Just aware enough: Evaluating awareness across artificial systemscited by 1
  11. 2026Agent4POI: Agentic Context-Conditioned Affordance Reasoning for Multimodal Point-of-Interest Recommendationcited by 1
  12. 2021Bayesian deep learning of affordances from RGB imagescited by 0
  13. 2024Contextual Affordances for Safe Exploration in Robotic Scenarioscited by 0
  14. 2024Affordance Perception by a Knowledge-Guided Vision-Language Model with Efficient Error Correctioncited by 0
  15. 2024Incremental Learning of Affordances using Markov Logic Networkscited by 0
  16. 2026Token-Based Affordance Grounding with Large Vision-Language Modelscited by 0