The increasing availability of online and mobile information platforms is facilitating the development of peer-to-peer collaboration strategies in large-scale networks. These technologies are being leveraged by networked robotic systems to provide applications of automated transport, resource redistribution (collaborative consumption), and location services. Yet, external observations of the system dynamics may expose sensitive information about the participants that compose these networks (robots, resources, and humans). In particular, we are concerned with settings where an adversary gains access to a snapshot of the dynamic state of the system. We propose a method that quantifies how easy it is for the adversary to identify the specific type of any agent (which can be a robot, resource, or human) in the network, based on this observation. We draw from the theory of differential privacy to propose a closed-form expression for the leakage of the system when the snapshot is taken at steady-state, as well as a numerical approach to compute the leakage when the snapshot is taken at any given time. The novelty of our approach is that our privacy model builds on a macroscopic description of the system's state, which allows us to take account of protected entities (network participants) that are interdependent. Our results show how the leakage varies, as a function of the composition and dynamic behavior of the network; they also indicate design rules for increasing privacy levels.
Federated Imitation Learning: A Privacy Considered Imitation Learning Framework for Cloud Robotic Systems with Heterogeneous Sensor Data
Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院）；University of Chinese Academy of Sciences（中国科学院大学）；Hong Kong University of Science and Technology（香港科技大学）
Depth-Only Open-Vocabulary 3D Semantic Segmentation For Privacy-Preserving Robotic Applications
Humanoid Robots Lab, University of Bonn（波恩大学仿人机器人实验室）；Lamarr Institute for Machine Learning and Artificial Intelligence（拉马尔机器学习和人工智能研究所）；Center for Robotics, Bonn, Germany（波恩机器人中心）
Position: Robot Privacy as Embodied Boundary Work. Connecting Capabilities, Contexts, and Design Responses in Everyday Robotics
The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；Tsinghua University（清华大学）；Beijing Academy of Artificial Intelligence（北京智源人工智能研究院）；The Hong Kong University of Science and Technology（香港科技大学）
Mixed Reality Environment and High-Dimensional Continuification Control for Swarm Robotics
Scuola Superiore Meridionale（南方高等学院）；University of Rome La Sapienza（罗马第一大学）；Tandon School of Engineering, New York University（纽约大学坦登工程学院）；University of Naples Federico II（那不勒斯费德里科二世大学）
FedVLA: Federated Vision-Language-Action Learning with Dual Gating Mixture-of-Experts for Robotic Manipulation
National University of Defense Technology（国防科技大学）；Bytedance Seed（字节跳动 Seed）；Shenzhen MSU-BIT University（深圳北理莫斯科大学）；Guangdong Laboratory of Artificial Intelligence and Digital Economy (SZ)（广东人工智能与数字经济实验室（深圳））
SoK: Security and Privacy of Foundation-Model-Powered Robots
College of Computing and Data Science, Nanyang Technological University（南洋理工大学计算与数据科学学院）；School of Cyber Science and Engineering, Wuhan University（武汉大学网络空间安全学院）
Privacy-Preserving Multi-Stage Fall Detection Framework with Semi-supervised Federated Learning and Robotic Vision Confirmation
Analytics Everywhere Lab, University of New Brunswick, Canada（分析无界实验室，新不伦瑞克大学，加拿大）；SE+AI Research Lab, University of New Brunswick, Canada（SE+AI研究实验室，新不伦瑞克大学，加拿大）；National Research Council, New Brunswick, Canada（国家研究理事会，新不伦瑞克，加拿大）；RMIT University, Australia（皇家墨尔本理工大学，澳大利亚）
Privacy-Preserving Map-Free Exploration for Confirming the Absence of a Radioactive Source
Princeton University（普林斯顿大学）；Program on Science and Global Security (SGS), Princeton（普林斯顿大学科学与全球安全项目）；Intelligent Robot Motion (IRoM) Lab, Princeton（普林斯顿大学智能机器人运动实验室）
The Adaptive Architectural Layout: How the Control of a Semi-Autonomous Mobile Robotic Partition was Shared to Mediate the Environmental Demands and Resources of an Open-Plan Office
Federated Joint Learning of Robot Networks in Stroke Rehabilitation
College of Aeronautics and Engineering, Kent State University（肯特州立大学航空与工程学院）；Kent State University（肯特州立大学）；New Jersey Institute of Technology（新泽西理工学院）；Cleveland Clinic（克利夫兰诊所）
