Humans naturally develop preferences for how manipulation tasks should be performed, which are often subtle, personal, and difficult to articulate. Although it is important for robots to account for these preferences to increase personalization and user satisfaction, they remain largely underexplored in robotic manipulation, particularly in the context of deformable objects like garments and fabrics. In this work, we study how to adapt pretrained visuomotor diffusion policies to reflect preferred behaviors using limited demonstrations. We introduce RKO, a novel preference-alignment method that combines the benefits of two recent frameworks: RPO and KTO. We evaluate RKO against common preference learning frameworks, including these two, as well as a baseline vanilla diffusion policy, on real-world cloth-folding tasks spanning multiple garments and preference settings. We show that preference-aligned policies (particularly RKO) achieve superior performance and sample efficiency compared to standard diffusion policy fine-tuning. These results highlight the importance and feasibility of structured preference learning for scaling personalized robot behavior in complex deformable object manipulation tasks.
AimBot: A Simple Auxiliary Visual Cue to Enhance Spatial Awareness of Visuomotor Policies
Computer Science and Engineering Department, University of Michigan（密歇根大学计算机科学与工程系）；Robotics Department, University of Michigan（密歇根大学机器人学系）；Lambda Labs（Lambda实验室）
KineDex: Learning Tactile-Informed Visuomotor Policies via Kinesthetic Teaching for Dexterous Manipulation
Tongji University（同济大学）；Tsinghua University（清华大学）；Shanghai Jiao Tong University（上海交通大学）；University of Hong Kong（香港大学）；Shanghai Qi Zhi Institute（上海启智研究院）；Shanghai AI Lab（上海人工智能实验室）
If a robot masters folding a kitchen towel, we would expect it to master folding a large beach towel. However, existing policy learning methods that rely on data augmentation still don't guarantee such generalization. Our insight is to add equivariance to both the visual object representation and policy architecture. We propose EquivAct which utilizes SIM(3)-equivariant network structures that guarantee generalization across all possible object translations, 3D rotations, and scales by construction. EquivAct is trained in two phases. We first pre-train a SIM(3)-equivariant visual representatio…
Risk-Aware Reinforcement Learning for Mobile Manipulation
Oxford Robotics Institute, Department of Engineering Science, University of Oxford（牛津大学机器人研究所、工程科学系、牛津大学）；Dyson Institute of Engineering & Technology（戴森工程与技术研究所）；Bristol Robotics Laboratory, School of Engineering, University of West England（布里斯托尔机器人实验室、工程学院、西英格兰大学）
DINOv3-Diffusion Policy: Self-Supervised Large Visual Model for Visuomotor Diffusion Policy Learning
Department of Computing and Mathematics, Manchester Metropolitan University（计算与数学系，曼彻斯特 Metropolitan 大学）；Department of Computing, Imperial College London（计算系，帝国理工学院伦敦分校）
FreqPolicy: Frequency Autoregressive Visuomotor Policy with Continuous Tokens
ShanghaiTech University（上海科技大学）；The University of Hong Kong（香港大学）；Digital Trust Centre, Nanyang Technological University（南洋理工大学数字信任中心）；The Chinese University of Hong Kong（香港中文大学）
A Study on Enhancing the Generalization Ability of Visuomotor Policies via Data Augmentation
University of Chinese Academy of Sciences（中国科学院大学）；School of Artificial Intelligence & Institute of Automation, Chinese Academy of Sciences & NLPR（中国科学院人工智能学院与自动化研究所）
Decompose and Reorganize: Planning with Primitives and Visuomotor Policies Learned from Demonstrations
The University of Hong Kong（香港大学）；JD.com (JingDong)（京东）；Nanyang Technological University (NTU)（南洋理工大学）；Southern University of Science and Technology (SUSTech)（南方科技大学）；Huawei Technologies（华为技术有限公司）；The Chinese University of Hong Kong (CUHK)（香港中文大学）
eVGGT: An Efficient Geometry-Aware Vision Encoder for Visuomotor Policies
Department of Computer Vision, Mohammed bin Zayed University of Artificial Intelligence（计算机视觉系，穆罕默德·本·扎耶德人工智能大学）；AIT Austrian Institute of Technology GmbH（奥地利技术研究所）
DemoSpeedup: Accelerating Visuomotor Policies via Entropy-Guided Demonstration Acceleration
Shanghai Qi Zhi Institute（上海启智研究院）；Tsinghua University IIIS（清华大学智能科学研究院）；Shanghai AI Lab（上海人工智能实验室）；University of Electronic Science and Technology of China（电子科技大学）
EmbodiSteer: Steering Embodiment-Agnostic Visuomotor Policies with Joint-Space Guidance for Zero-Shot Cross-Embodiment Deployment
Department of Automation, Tsinghua University（清华大学自动化系）；Beijing Key Laboratory of Embodied Intelligence Systems（北京具身智能系统重点实验室）；Institute for Embodied Intelligence and Robotics, Tsinghua University（清华大学具身智能与机器人研究所）
