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Cited by 40Robot Manipulation and Learning3D Shape Modeling and AnalysisReinforcement Learning in Robotics

AdaAfford: Learning to Adapt Manipulation Affordance for 3D Articulated Objects via Few-shot Interactions

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Wang, Yian, Wu, Ruihai, Mo, Kaichun, Ke, Jiaqi, Fan, Qingnan, Guibas, Leonidas J., Dong, Hao·2022·Lecture Notes in Computer Science
DOI 10.1007/978-3-031-19818-2_6
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Authors
Wang, Y., Wu, R., Mo, K., Ke, J., Fan, Q., Guibas, L. J., & Dong, H.
Affiliations
  • Peking University (CN)
  • Stanford University (US)
Corresponding author
Dong, Hao
ORCID
Venue
Lecture Notes in Computer Science
Publisher
Springer Nature Switzerland
Published
2022
Pages
90-107
ISSN
  • 0302-9743 (ISSN-L)
  • 1611-3349
DOI
10.1007/978-3-031-19818-2_6
OpenAlex
W4312363187
Cited by
40
References
35
Citations by year
2026: 4 · 2025: 9 · 2024: 13 · 2023: 14
FWCI
13.74
Type
conference paper
Language
en
Open access
Licence
Locations
  • Lecture notes in computer science (publishedVersion)
  • arXiv (Cornell University) (submittedVersion)
  • arXiv (Cornell University)
Funders
  • National Science Foundation (1763268)
  • Toyota Research Institute
  • National Natural Science Foundation of China (62006006)
Topics
  • Physical Sciences › Engineering › Control and Systems Engineering › Robot Manipulation and Learning
  • Physical Sciences › Engineering › Computational Mechanics › 3D Shape Modeling and Analysis
  • Physical Sciences › Computer Science › Artificial Intelligence › Reinforcement Learning in Robotics
Keywords
articulated objects, few-shot learning, kinematic constraints, joint limits, joint friction, robotic manipulation, 3D object interaction

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