This paper addresses the challenging problem of energy-efficient and uncertainty-aware pose estimation in insect-scale drones, which is crucial for tasks such as surveillance in constricted spaces and for enabling non-intrusive spatial intelligence in smart homes. Since tiny drones operate in highly dynamic environments, where factors like lighting and human movement impact their predictive accuracy, it is crucial to deploy uncertainty-aware prediction algorithms that can account for environmental variations and express not only the prediction but also confidence in the prediction. We address both of these challenges with Compute-in-Memory (CIM) which has become a pivotal technology for deep learning acceleration at the edge. While traditional CIM techniques are promising for energy-efficient deep learning, to bring in the robustness of uncertainty-aware predictions at the edge, we introduce a suite of novel techniques: First, we discuss CIM-based acceleration of Bayesian filtering methods uniquely by leveraging the Gaussian-like switching current of CMOS inverters along with co-design of kernel functions to operate with extreme parallelism and with extreme energy efficiency. Secondly, we discuss the CIM-based acceleration of variational inference of deep learning models through probabilistic processing while unfolding iterative computations of the method with a compute reuse strategy to significantly minimize the workload. Overall, our co-design methodologies demonstrate the potential of CIM to improve the processing efficiency of uncertainty-aware algorithms by orders of magnitude, thereby enabling edge robotics to access the robustness of sophisticated prediction frameworks within their extremely stringent area/power resources.
Learning Autonomy: Off-Road Navigation Enhanced by Human Input
J. Mike Walker ’66 Department of Mechanical Engineering, Texas A&M University（J. Mike Walker ’66 机械工程系，德克萨斯A&M大学）；Department of Engineering technology and Industrial Distribution, Texas A&M University（工程技术与工业分销系，德克萨斯A&M大学）
OpenRC: An Open-Source Robotic Colonoscopy Framework for Multimodal Data Acquisition and Autonomy Research
Walker Department of Mechanical Engineering, The University of Texas at Austin（德克萨斯大学奥斯汀分校沃克机械工程系）；Department of Surgical Oncology, Division of Surgery, The University of Texas MD Anderson Cancer Center（德克萨斯大学MD安德森癌症中心外科肿瘤学系）；School of Medicine and Health, Technical University of Munich（慕尼黑工业大学医学与健康学院）
MOSAIC: Modular Scalable Autonomy for Intelligent Coordination of Heterogeneous Robotic Teams
FZI Forschungszentrum für Informatik, Karlsruhe, Germany（弗赖堡信息研究中心，卡尔斯鲁厄，德国）；Robotic Systems Lab (RSL), ETH Zürich, Zürich, Switzerland（机器人系统实验室（RSL），苏黎世联邦理工学院，苏黎世，瑞士）；Machine Intelligence and Robotics Lab (MaiRo), Karlsruhe Institute for Technology (KIT), Karlsruhe, Germany（机器智能与机器人实验室（MaiRo），卡尔斯鲁厄技术学院（KIT），卡尔斯鲁厄，德国）；European Space Agency (ESA), Noordwijk, Netherlands（欧洲航天局（ESA），诺德威克，荷兰）
California State Polytechnic University, Pomona（加州州立理工大学波莫纳分校）；Jet Propulsion Laboratory, California Institute of Technology（加州理工学院喷气推进实验室）；Brunel University London（伦敦布鲁内尔大学）
Before Autonomy Takes Control: Software Testing in Robotics
Ruhr University Bochum Bochum Germany；University of Bremen Bremen Germany；IT University of Copenhagen Copenhagen Denmark；Chalmers\,|\,Gothenburg University Gothenburg Sweden；Ruhr University Bochum；University of Bremen；IT University of Copenhagen；Chalmers\,|\,Gothenburg University
Results from the Robocademy ITN: Autonomy, Disturbance Rejection and Perception for Advanced Marine Robotics
Heriot-Watt University（赫瑞-瓦特大学）；DFKI（德国人工智能研究中心）；National Oceanography Centre（英国国家海洋学中心）；National Technical University of Athens（雅典国立技术大学）；Tallin University of Technology（塔林理工大学）；Atlas Elektronik GmbH（阿特拉斯电子有限公司）；University of Girona（赫罗纳大学）；SeeByte（SeeByte公司）
海洋和水下资源是许多国家经济的重要组成部分。这需要大量的财政资源用于其建设和维护。机器人技术有望填补这一空白，通过自动化和/或将人类从恶劣的环境中移除，以便轻松执行维护任务。Robocademy Marie Sklodowska-Curie初始培训网络由欧盟FP 7研究计划资助，旨在训练13名研究员成为海洋和水下机器人领域的世界领先研究人员。研究员将指导性研究发展到三个关键领域：自主性，干扰拒绝和感知。本文概述了研究员在这三个行动方针方面的研究。71份科学出版物是该项目的主要成果，目前还有许多其他出版物正在筹备中。大多数研究员在欧洲找到了工作，这表明对这类专家的需求很大。我们相信，该项目的成果已经对海洋机器人行业产生了影响，因为关键技术已经被采用。
Robotic Navigation Autonomy for Subretinal Injection via Intelligent Real-Time Virtual iOCT Volume Slicing
Technische Universität München（慕尼黑工业大学）；Johns Hopkins University（约翰斯·霍普金斯大学）；Wilmer Eye Institute（威尔默眼科研究所）；Johns Hopkins Hospital（约翰斯·霍普金斯医院）；Technical University of Munich（慕尼黑工业大学）；Whiting School of Engineering（怀廷工程学院）
Hi-Dyna Graph: Hierarchical Dynamic Scene Graph for Robotic Autonomy in Human-Centric Environments
School of Computer Science, Fudan University（复旦大学计算机科学学院）；Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University（复旦大学脑启发式智能科学研究院）
Human-Centered Shared Autonomy for Motor Planning, Learning, and Control Applications
Translational Neurorobotics Laboratory（转化神经机器人实验室）；Department of Electrical, Computer, and Biomedical Engineering（电气、计算机与生物医学工程系）；University of Rhode Island（罗德岛大学）；Human-Centered AI (HCAI) Labs（以人为本的人工智能实验室）
