Tool use requires reasoning about the fit between an object's affordances and the demands of a task. Visual affordance learning can benefit from goal-directed interaction experience, but current techniques rely on human labels or expert demonstrations to generate this data. In this paper, we describe a method that grounds affordances in physical interactions instead, thus removing the need for human labels or expert policies. We use an efficient sampling-based method to generate successful trajectories that provide contact data, which are then used to reveal affordance representations. Our framework, GIFT, operates in two phases: first, we discover visual affordances from goal-directed interaction with a set of procedurally generated tools; second, we train a model to predict new instances of the discovered affordances on novel tools in a self-supervised fashion. In our experiments, we show that GIFT can leverage a sparse keypoint representation to predict grasp and interaction points to accommodate multiple tasks, such as hooking, reaching, and hammering. GIFT outperforms baselines on all tasks and matches a human oracle on two of three tasks using novel tools.
GIFT: Guided Intermediate Feature Training via Action-Oriented Structural Supervision for Robotic Manipulation
Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；University of Chinese Academy of Sciences（中国科学院大学）；National University of Singapore（新加坡国立大学）；Tsinghua University（清华大学）；Fudan University（复旦大学）
该研究提出 GIFT 框架，通过几何对齐、可供性预测和目标区域重建约束中间特征，在 LIBERO-Plus 和 RoboCasa 数据集上，其多个变体在机器人操作任务中显著优于基准模型，性能提升明显。
AffordDexGrasp: Open-set Language-guided Dexterous Grasp with Generalizable-Instructive Affordance
School of Computer Science and Engineering, Sun Yat-sen University, China（中山大学计算机科学与工程学院）；Key Laboratory of Machine Intelligence and Advanced Computing, Ministry of Education, China（教育部人工智能与高级计算重点实验室）
Affordance-R1: Reinforcement Learning for Generalizable Affordance Reasoning in Multimodal Large Language Model
The Hong Kong University of Science and Technology (GZ)（香港科技大学（广州））；National University of Singapore（新加坡国立大学）；ShanghaiTech University（上海科技大学）；East China Normal University（华东师范大学）；Nanjing University of Information Science & Technology（南京信息工程大学）；Zhejiang University（浙江大学）；Institute of Automation, Chinese Academy of Science（中国科学院自动化研究所）；Shanghai AI Laboratory（上海人工智能实验室）
Afford-X: Generalizable and Slim Affordance Reasoning for Task-oriented Manipulation
Institute for Artificial Intelligence, Peking University（北京大学人工智能研究院）；Department of Computer Science and Engineering, Hong Kong University of Science and Technology（香港科技大学计算机科学与工程系）；Shenyang Institute of Automation, Chinese Academy of Sciences（中国科学院沈阳自动化研究所）；Institute for Al Industry Research, Tsinghua University（清华大学人工智能产业研究院）；Department of Computer Science, Tsinghua University（清华大学计算机科学系）
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）
FUNCanon: Learning Pose-Aware Action Primitives via Functional Object Canonicalization for Generalizable Robotic Manipulation
TAMS (Technical Aspects of Multimodal Systems), Department of Informatics, University of Hamburg（汉堡大学信息学院TAMS（多模态系统技术））；Technical University of Munich（慕尼黑技术大学）；Agile Robots SE（敏捷机器人有限公司）
