Robots have the potential to improve health monitoring outcomes for the elderly by providing doctors, and caregivers with information about the person's behavior, health activities and their surrounding environment. Over the years, less work has been done to enable robots to preserve information for longer periods of time, on the order of months and years of data, and use this contextual information to answer queries. Time complexity to process this massive sensor data in a timely fashion, inability to anticipate the future queries in advance and imprecision involved in the results have been the main impediments in making progress in this area. We make a contribution by introducing RoboMem, a query answering system for health-care assistance of elderly over long term; continuous data feeds that intends to overcome the challenges of giving long term memory to robots. The design for our framework preprocesses the sensor data and stores this preprocessed data into the database. This data is updated in the database by going through successive refinements, improving its accuracy for responding to queries. If data in the database is not enough to answer a query, a small set of relevant frames (also obtained from the database) will be reprocessed to obtain the answer. [Our initial prototype of RoboMem stores 3.5MB of data in the database as compared to 535.8MB of actual video frames and with minimal data in the database it is able to fetch information fundamental to respond to queries in 0.0002 seconds on average].
RoboMemory: A Brain-inspired Multi-memory Agentic Framework for Interactive Environmental Learning in Physical Embodied Systems
FNii-Shenzhen（FNii-深圳）；SSE, CUHK-Shenzhen（SSE，CUHK-深圳）；The Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳））；The University of Hong Kong（香港大学）；National University of Singapore（新加坡国立大学）；The Chinese University of Hong Kong（香港中文大学）；Ising AI
Your Robot Will Feel You Now: Empathy in Robots and Embodied Agents
School of Computing Science, Simon Fraser University（计算科学学院，西蒙弗雷泽大学）；School of Interactive Arts and Technology, Simon Fraser University（交互艺术与技术学院，西蒙弗雷泽大学）
Steering a Predator Robot using a Mixed Frame/Event-Driven Convolutional Neural Network
Institute of Neuroinformatics, University of Zürich and ETH Zürich（苏黎世大学与苏黎世联邦理工学院神经信息学研究所）；Intelligent Systems Research Centre, University of Ulster（阿尔斯特大学智能系统研究中心）
Real Robot Challenge 2022: Learning Dexterous Manipulation from Offline Data in the Real World
Max Planck Institute for Intelligent Systems（马克斯·普朗克智能系统研究所）；Helmholtz and TU Munich（亥姆霍兹与慕尼黑工业大学）；Harvard University（哈佛大学）；DeepMind（深度思维）；University of Toronto（多伦多大学）；University College Dublin（都柏林大学学院）；Pohang University of Science and Technology（浦项科技大学）；Electronics and Telecommunications Research Institute（电子与电信研究所）；ETH Zurich（苏黎世联邦理工学院）
本文介绍了 Real Robot Challenge 2022，该竞赛通过提供真实机器人离线数据集和远程评估平台，促进了强化学习与机器人社区的交流，并比较了获胜团队与最先进离线 RL 算法在灵巧操作任务上的表现。
