Enabling robotic manipulation that generalizes to out-of-distribution scenes is a crucial step toward open-world embodied intelligence. For human beings, this ability is rooted in the understanding of semantic correspondence among objects, which naturally transfers the interaction experience of familiar objects to novel ones. Although robots lack such a reservoir of interaction experience, the vast availability of human videos on the Internet may serve as a valuable resource, from which we extract an affordance memory including the contact points. Inspired by the natural way humans think, we propose Robo-ABC: when confronted with unfamiliar objects that require generalization, the robot can acquire affordance by retrieving objects that share visual or semantic similarities from the affordance memory. The next step is to map the contact points of the retrieved objects to the new object. While establishing this correspondence may present formidable challenges at first glance, recent research finds it naturally arises from pre-trained diffusion models, enabling affordance mapping even across disparate object categories. Through the Robo-ABC framework, robots may generalize to manipulate out-of-category objects in a zero-shot manner without any manual annotation, additional training, part segmentation, pre-coded knowledge, or viewpoint restrictions. Quantitatively, Robo-ABC significantly enhances the accuracy of visual affordance retrieval by a large margin of 31.6% compared to state-of-the-art (SOTA) end-to-end affordance models. We also conduct real-world experiments of cross-category object-grasping tasks. Robo-ABC achieved a success rate of 85.7%, proving its capacity for real-world tasks.
RoboPCA: Pose-centered Affordance Learning from Human Demonstrations for Robot Manipulation
Key Laboratory of AI Safety of CAS, Institute of Computing Technology, Chinese Academy of Sciences（中国科学院人工智能安全重点实验室、计算技术研究所）；University of Chinese Academy of Sciences（中国科学院大学）
AffordanceWAM: Affordance-Aware Joint World-Action Modeling for Robot Manipulation
The University of Hong Kong（香港大学）；The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；Peking University（北京大学）；The Chinese University of Hong Kong（香港中文大学）；National University of Singapore（新加坡国立大学）；Knowin AI（考拉悠然科技）
RoboAfford++: A Generative AI-Enhanced Dataset for Multimodal Affordance Learning in Robotic Manipulation and Navigation
Xiaomi EV（小米电动车）；Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；Tsinghua Shenzhen International Graduate School, Tsinghua University（清华大学深圳国际研究生院）；Gaoling School of Artificial Intelligence, Renmin University of China（中国人民大学 Gallagher人工智能学院）；School of Computer Science and Information Engineering, Hefei University of Technology（合肥工业大学计算机科学与信息工程学院）
RAGNet: Large-scale Reasoning-based Affordance Segmentation Benchmark towards General Grasping
The Chinese University of Hong Kong（香港中文大学）；Institute of Computing Technology, Chinese Academy of Sciences（中国科学院计算技术研究所）；Dexmal；Mohamed bin Zayed University of Artificial Intelligence（穆罕默德·本·扎耶德人工智能大学）；SKL-IOTSC, CIS, University of Macau（澳门科学馆-物联网与智能系统研究中心，澳门大学）
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）
3D Affordance Keypoint Detection for Robotic Manipulation
Advanced Robotics Centre, National University of Singapore（新加坡国立大学先进机器人中心）；Department of Mechanical Engineering, National University of Singapore（新加坡国立大学机械工程系）
TRACER: Texture-Robust Affordance Chain-of-Thought for Deformable-Object Region Grounding
School of Artificial Intelligence and Robotics, Hunan University（人工智能与机器人学院，湖南大学）；National Engineering Research Center of Robot Visual Perception and Control Technology, Hunan University（机器人视觉感知与控制技术国家工程研究中心，湖南大学）
Learning Granularity-Aware Affordances from Human-Object Interaction for Tool-Based Functional Dexterous Grasping
School of Artificial Intelligence and Robotics, Hunan University（人工智能与机器人学院，湖南大学）；National Engineering Research Center of Robot Visual Perception and Control Technology, Hunan University（机器人视觉感知与控制技术国家工程研究中心，湖南大学）
Affordance Field Intervention: Enabling VLAs to Escape Memory Traps in Robotic Manipulation
School of Computer Science, The University of Sydney（悉尼大学计算机科学学院）；John Hopcropt Center for Computer Science, Shanghai Jiao Tong University（上海交通大学计算机科学中心）
EqvAfford: SE(3) Equivariance for Point-Level Affordance Learning
School of EECS, PKU（北京大学工程与计算机科学学院）；CFCS, School of CS, PKU（北京大学计算机学院计算与认知科学中心）；National Key Laboratory for Multimedia Information Processing, School of CS, PKU（北京大学计算机学院多媒体信息处理国家重点实验室）
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）
PALM: Progress-Aware Policy Learning via Affordance Reasoning for Long-Horizon Robotic Manipulation
University of Pennsylvania（宾夕法尼亚大学）；University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；Nanyang Technological University（南洋理工大学）；University of Oxford（牛津大学）；Massachusetts Institute of Technology（麻省理工学院）
Open-Vocabulary Affordance Detection using Knowledge Distillation and Text-Point Correlation
FPT Software AI Center（FPT软件人工智能中心）；TU Wien（维也纳工业大学）；Imperial College London（帝国理工学院）；University of Arkansas（阿肯色大学）；Ton Duc Thang University（孙德胜大学）；University of Liverpool（利物浦大学）
Multi-Keypoint Affordance Representation for Functional Dexterous Grasping
School of Artificial Intelligence and Robotics, Hunan University（人工智能与机器人学院，湖南大学）；College of Computer Science and Electronic Engineering, Hunan University（计算机科学与电子工程学院，湖南大学）；National Engineering Research Center of Robot Visual Perception and Control Technology, Hunan University（机器人视觉感知与控制技术国家工程研究中心，湖南大学）
UniAff: A Unified Representation of Affordances for Tool Usage and Articulation with Vision-Language Models
Shanghai Jiao Tong University（上海交通大学）；Hong Kong University of Science and Technology（香港科技大学）；National University of Singapore（新加坡国立大学）；Rutgers University（罗格斯大学）；Hefei University of Technology（合肥工业大学）；Shanghai AI Lab（上海人工智能实验室）
