Learning from Demonstrations (LfD) enables robots to learn object manipulation directly from human demonstrations. In contrast to resource-intensive and labor-intensive teleoperation, egocentric demonstrations are easy to collect, enabling scalable data collection. Manipulated objects are often partially occluded in everyday environments, yet prior egocentric demonstration-based methods that require no robots or specialized teaching devices do not explicitly address occlusions. To fill this gap, we propose EgoReveal, a framework that learns viewpoint-repositioning together with manipulation actions from egocentric human demonstrations to reposition the viewpoint and obtain target visibility under partial occlusions. EgoReveal instantiates this as two imitation policies: one predicts the future camera-arm pose sequence from the current view and camera-arm pose, and the other predicts the future control commands from object keypoints and a history of control commands. EgoReveal switches between the two based on target visibility. In experiments where the manipulated object is partially occluded initially, EgoReveal repositions the camera to obtain visibility and completes the task more reliably than fixed-camera baselines.
EgoGuide: Egocentric Guidance for Efficient Robot-Free Demonstration Collection and Learning
Shanghai Jiao Tong University（上海交通大学）；Shanghai Innovation Institute（上海创新研究院）；Beijing Institute for General Artificial Intelligence (BIGAI)（北京通用人工智能研究院）
The University of Hong Kong（香港大学）；Microsoft Research Asia（微软亚洲研究院）；TransGP；Texas A&M University（德克萨斯农工大学）
Understanding dynamic hand motions and actions from egocentric RGB videos is a fundamental yet challenging task due to self-occlusion and ambiguity. To address occlusion and ambiguity, we develop a transformer-based framework to exploit temporal information for robust estimation. Noticing the different temporal granularity of and the semantic correlation between hand pose estimation and action recognition, we build a network hierarchy with two cascaded transformer encoders, where the first one exploits the short-term temporal cue for hand pose estimation, and the latter aggregates per-frame po…
TPT-Bench: A Large-Scale, Long-Term and Robot-Egocentric Dataset for Benchmarking Target Person Tracking
Robotics and Computer Vision Laboratory, Southern University of Science and Technology, Shenzhen, China（机器人与计算机视觉实验室，南方科技大学，深圳，中国）；Human-Robot Interfaces and Interaction Laboratory, Italian Institute of Technology, Geona, Italy（人机交互实验室，意大利理工学院，Geona，意大利）；Robotics Perception and Intelligence Laboratory, Southern University of Science and Technology, Shenzhen, China（机器人感知与智能实验室，南方科技大学，深圳，中国）；Chen Kar-Shun Robotics Institute, Hong Kong University of Science and Technology, Hong Kong SAR, China（陈卡尔顺机器人研究所，香港科技大学，香港特别行政区，中国）
KiVi: Kinesthetic-Visuospatial Integration for Dynamic and Safe Egocentric Legged Locomotion
MARMot Lab, National University of Singapore（新加坡国立大学MARMot实验室）；Center of X-Mechanics, Zhejiang University（浙江大学X力学中心）；Robot and Robot Intelligence Lab, Zhejiang University（浙江大学机器人与机器人智能实验室）
Learning Sim-Grounded Policies for Bimanual Rope Manipulation from Human Teleoperation Data
Technical University of Darmstadt（德累斯顿技术大学）；German Research Center for Artificial Intelligence (DFKI)（德国人工智能研究中心）；Robotics Institute Germany (RIG)（德国机器人研究所）；Centre for Cognitive Science（认知科学研究中心）；Honda Research Institute Europe GmbH（本田欧洲研究院）
Face versus Body Tracking for Human-Robot Interaction: An Egocentric Dataset
Furhat Robotics；University of Naples Federico II（那不勒斯费德里科二世大学）；Division of Speech, Music and Hearing, KTH Royal Institute of Technology（语音、音乐和听觉研究所，皇家理工学院）
Egocentric World Model for Photorealistic Hand-Object Interaction Synthesis
Texas A&M University（德克萨斯A&M大学）；University of Minnesota（明尼苏达大学）；University of Wisconsin–Madison（威斯康星大学麦迪逊分校）；University of California, Los Angeles（加州大学洛杉矶分校）；University of Texas at Austin（得克萨斯大学奥斯汀分校）；Amazon（亚马逊）；State University of New York at Stony Brook（纽约州立大学石溪分校）
EgoEV-HandPose: Egocentric 3D Hand Pose Estimation and Gesture Recognition with Stereo Event Cameras
National Research Center for Optical Instrumentation, Zhejiang University（浙江大学光学仪器国家研究中心）；School of Artificial Intelligence and Robotics and the National Engineering Research Center of Robot Visual Perception and Control Technology, Hunan University（湖南大学人工智能与机器人学院和机器人视觉感知与控制技术国家工程研究中心）；Ant Group Company Ltd.（蚂蚁集团有限公司）
