Articulated Object Manipulation with Coarse-to-fine Affordance for Mitigating the Effect of Point Cloud Noise
Hyperplane Lab, School of CS, Peking University（北京大学计算机学院超平面实验室）；National Key Laboratory for Multimedia Information Processing（多媒体信息处理国家重点实验室）；Huawei（华为）
3D articulated objects are inherently challenging for manipulation due to the varied geometries and intricate functionalities associated with articulated objects.Point-level affordance, which predicts the per-point actionable score and thus proposes the best point to interact with, has demonstrated excellent performance and generalization capabilities in articulated object manipulation. However, a significant challenge remains: while previous works use perfect point cloud generated in simulation, the models cannot directly apply to the noisy point cloud in the real-world. To tackle this challenge, we leverage the property of real-world scanned point cloud that, the point cloud becomes less noisy when the camera is closer to the object. Therefore, we propose a novel coarse-to-fine affordance learning pipeline to mitigate the effect of point cloud noise in two stages. In the first stage, we learn the affordance on the noisy far point cloud which includes the whole object to propose the approximated place to manipulate. Then, we move the camera in front of the approximated place, scan a less noisy point cloud containing precise local geometries for manipulation, and learn affordance on such point cloud to propose fine-grained final actions. The proposed method is thoroughly evaluated both using large-scale simulated noisy point clouds mimicking real-world scans, and in the real world scenarios, with superiority over existing methods, demonstrating the effectiveness in tackling the noisy real-world point cloud problem.
Learning Environment-Aware Affordance for 3D Articulated Object Manipulation under Occlusions
CFCS, School of CS, PKU（计算机学院，北京大学）；School of EECS, PKU（电子工程系，北京大学）；University of Oxford（牛津大学）；National Key Laboratory for Multimedia Information Processing, School of CS, PKU（多媒体信息处理国家重点实验室，计算机学院，北京大学）
Adaptive Articulated Object Manipulation On The Fly with Foundation Model Reasoning and Part Grounding
Beijing University of Posts and Telecommunications（北京邮电大学）；School of Computer Science, Peking University（北京大学计算机学院）；School of EECS, Peking University（北京大学电子工程学院）
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）
Sim2Real$^2$: Actively Building Explicit Physics Model for Precise Articulated Object Manipulation
Tsinghua University（清华大学）；Beijing University of Posts and Telecommunications（北京邮电大学）；AVIC Chengdu Aircraft Industrial (Group) Co., Ltd（中航工业成都飞机工业（集团）有限责任公司）
DexSim2Real$^{2}$: Building Explicit World Model for Precise Articulated Object Dexterous Manipulation
Department of Mechanical Engineering, Tsinghua University（清华大学机械工程系）；Institute for Robotics and Intelligent Machines (IRIM), Georgia Institute of Technology（机器人与智能机器研究所（IRIM），佐治亚理工学院）；JD Explore Academy, Beijing, China（京东探索研究院，北京，中国）
Mitigating Cross-Modal Distraction and Ensuring Geometric Feasibility via Affordance-Guided and Self-Consistent MLLMs for Task Planning in Instruction-Following Manipulation
Department of Computer Science, National Yang Ming Chiao Tung University（计算机科学系，阳明交通大学）；Department of Computer Science and Information Engineering, National Taiwan University（计算机科学与信息工程系，台湾大学）
Hierarchical Reinforcement Learning for Articulated Tool Manipulation with Multifingered Hand
State Key Laboratory of Mechanical System and Vibration, School of Mechanical Engineering, Shanghai Jiao Tong University（机械系统与振动国家重点实验室，机械工程学院，上海交通大学）；Shanghai Key Laboratory of Intelligent Robotics, Meta Robotics Institute, Shanghai Jiao Tong University（智能机器人上海重点实验室，元机器人研究院，上海交通大学）
Online Estimation and Manipulation of Articulated Objects
RIPL-Lab, University of Waterloo, Canada（滑铁卢大学）；Robot Learning Lab, University of Freiburg, Germany（弗赖堡大学）；School of Informatics, University of Edinburgh, UK（爱丁堡大学）
Part-Guided 3D RL for Sim2Real Articulated Object Manipulation
Beijing National Research Center for Information Science and Technology (BNRist)（北京信息科学与技术国家研究中心（BNRist））；Shanghai AI Laboratory（上海人工智能实验室）；University of California San Diego（加利福尼亚大学圣迭戈分校）；Tsinghua University（清华大学）
General Flow as Foundation Affordance for Scalable Robot Learning
Institute for Interdisciplinary Information Sciences, Tsinghua University（清华大学交叉信息研究院）；Shanghai Qi Zhi Institute（上海期智研究院）；Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）
TONAV: Task-Oriented Navigation and Action-Velocity Chunk Learning for Articulated Object Quadrupedal Mobile Manipulation
School of Artificial Intelligence and Robotics, Hunan University（湖南大学人工智能与机器人学院）；National Engineering Research Center of Robot Visual Perception and Control Technology, Hunan University（湖南大学机器人视觉感知与控制技术国家工程研究中心）；College of Semiconductors (College of Integrated Circuits), Hunan University（湖南大学半导体学院（集成电路学院））
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。
