Affordance Transfer Across Object Instances via Semantically Anchored Functional Map
Robotics Institute, Carnegie Mellon University（卡内基梅隆大学机器人研究所）；School of Computer Science, The University of Sydney（悉尼大学计算机科学学院）；Australian Centre for Robotics, The University of Sydney（悉尼大学机器人中心）；College of Connected Computing, Vanderbilt University（范德比大学连接计算学院）
Traditional learning from demonstration (LfD) generally demands a cumbersome collection of physical demonstrations, which can be time-consuming and challenging to scale. Recent advances show that robots can instead learn from human videos by extracting interaction cues without direct robot involvement. However, a fundamental challenge remains: how to generalize demonstrated interactions across different object instances that share similar functionality but vary significantly in geometry. In this work, we propose \emph{Semantic Anchored Functional Maps} (SemFM), a framework for transferring affordances across objects from a single visual demonstration. Starting from a coarse mesh reconstructed from an image, our method identifies semantically corresponding functional regions between objects, selects mutually exclusive semantic anchors, and propagates these constraints over the surface using a functional map to obtain a dense, semantically consistent correspondence. This enables demonstrated interaction regions to be transferred across geometrically diverse objects in a lightweight and interpretable manner. Experiments on synthetic object categories and real-world robotic manipulation tasks show that our approach enables accurate affordance transfer with modest computational cost, making it well-suited for practical robotic perception-to-action pipelines.
SemAnCorr: Semantic Anchored Correspondence for Zero-Shot Manipulation Skill Transfer
Robotics Institute, Carnegie Mellon University（卡内基梅隆大学机器人研究所）；College of Connected Computing, Vanderbilt University（范德堡大学互联计算学院）；College of Engineering, University of California, Berkeley（加州大学伯克利分校工程学院）；School of Computer Science, The University of Sydney（悉尼大学计算机学院）
DORA: Object Affordance-Guided Reinforcement Learning for Dexterous Robotic Manipulation
TAMS (Technical Aspects of Multimodal Systems), Department of Informatics, Universität Hamburg（技术多模态系统（TAMS），信息学院，汉堡大学）；Agile Robots AG（敏捷机器人公司）；Technical University of Munich（慕尼黑技术大学）
AnchorDP3: 3D Affordance Guided Sparse Diffusion Policy for Robotic Manipulation
Jingdong Technology Information Technology Co., Ltd（京东科技信息技术有限公司）；Tsinghua University（清华大学）；Fudan University（复旦大学）；Southeast University（东南大学）；University of Science and Technology of China（中国科学技术大学）
AffordGrasp: Cross-Modal Diffusion for Affordance-Aware Grasp Synthesis
ShanghaiTech University（上海科技大学）；Shanghai Engineering Research Center of Intelligent Vision and Imaging（上海智能视觉与成像工程技术研究中心）；University of Science and Technology of China（中国科学技术大学）
Task-Aware Bimanual Affordance Prediction via VLM-Guided Semantic-Geometric Reasoning
Department of Computer Science, Technical University of Darmstadt（达姆施塔特工业大学计算机科学系）；German Research Center for AI (DFKI)（德国人工智能研究中心）；Centre for Cognitive Science, Technical University of Darmstadt（达姆施塔特工业大学认知科学中心）；Hessian Center for Artificial Intelligence (Hessian.AI), Darmstadt（黑森州人工智能中心）；Robotics Institute Germany (RIG)（德国机器人研究所）
