Embodied AI: Emerging Risks and Opportunities for Policy Action
Centre for the Governance of AI（治理人工智能中心）；French Center for AI Safety (CeSIA)（法国人工智能安全中心）；Carnegie Mellon University（卡内基梅隆大学）；University of Oxford（牛津大学）；University of Bologna（博洛尼亚大学）；Tony Blair Institute for Global Change（托尼·布莱尔全球变革研究所）；Yale University（耶鲁大学）；Yale Digital Ethics Center（耶鲁数字伦理中心）
Jared Perlo, Alexander Robey, Fazl Barez, Luciano Floridi, Jakob Mökander
The field of embodied AI (EAI) is rapidly advancing. Unlike virtual AI, EAI systems can exist in, learn from, reason about, and act in the physical world. With recent advances in AI models and hardware, EAI systems are becoming increasingly capable across wider operational domains. While EAI systems can offer many benefits, they also pose significant risks, including physical harm from malicious use, mass surveillance, as well as economic and societal disruption. These risks require urgent attention from policymakers, as existing policies governing industrial robots and autonomous vehicles are insufficient to address the full range of concerns EAI systems present. To help address this issue, this paper makes three contributions. First, we provide a taxonomy of the physical, informational, economic, and social risks EAI systems pose. Second, we analyze policies in the US, EU, and UK to assess how existing frameworks address these risks and to identify critical gaps. We conclude by offering policy recommendations for the safe and beneficial deployment of EAI systems, such as mandatory testing and certification schemes, clarified liability frameworks, and strategies to manage EAI's potentially transformative economic and societal impacts.
Tokyo Institute of Technology（东京工业大学）；RIKEN Center for Advanced Intelligence Project (AIP)（日本理化学研究所先进智能项目中心）；gDial, Inc.（gDial公司）
Artificial intelligences (AI) are increasingly being embodied and embedded in the world to carry out tasks and support decision-making with and for people. Robots, recommender systems, voice assistants, virtual humans - do these disparate types of embodied AI have something in common? Here we show how they can manifest as "socially embodied AI." We define this as the state that embodied AI "circumstantially" take on within interactive contexts when perceived as both social and agentic by people. We offer a working ontology that describes how embodied AI can dynamically transition into socially…
Large Language Models for Robotics: Opportunities, Challenges, and Perspectives
Northwestern Polytechnical University（西北工业大学）；The University of Georgia（佐治亚大学）；Shaanxi Normal University（陕西师范大学）；Massachusetts General Hospital and Harvard Medical School（麻省总医院和哈佛医学院）
Massachusetts Institute of Technology（麻省理工学院）；University of Oxford（牛津大学）；University of Toronto（多伦多大学）；Waverly；Mila；University of Montreal（蒙特利尔大学）；Stanford University（斯坦福大学）；Technische Universität Berlin（柏林工业大学）；Element AI – A ServiceNow Company（Element AI – ServiceNow旗下公司）；University of Washington（华盛顿大学）；University of Pennsylvania（宾夕法尼亚大学）；McGill University（麦吉尔大学）；[Google] X
Machine learning has long since become a keystone technology, accelerating science and applications in a broad range of domains. Consequently, the notion of applying learning methods to a particular problem set has become an established and valuable modus operandi to advance a particular field. In this article we argue that such an approach does not straightforwardly extended to robotics -- or to embodied intelligence more generally: systems which engage in a purposeful exchange of energy and information with a physical environment. In particular, the purview of embodied intelligent agents ext…
Recent advances in the areas of multimodal machine learning and artificial intelligence (AI) have led to the development of challenging tasks at the intersection of Computer Vision, Natural Language Processing, and Embodied AI. Whereas many approaches and previous survey pursuits have characterised one or two of these dimensions, there has not been a holistic analysis at the center of all three. Moreover, even when combinations of these topics are considered, more focus is placed on describing, e.g., current architectural methods, as opposed to also illustrating high-level challenges and oppor…
Nanyang Technological University（南洋理工大学）；Singapore University of Technology and Design（新加坡科技设计大学）；Institute for Infocomm Research, A*STAR（新加坡科技研究局资讯通信研究院）
There has been an emerging paradigm shift from the era of "internet AI" to "embodied AI", where AI algorithms and agents no longer learn from datasets of images, videos or text curated primarily from the internet. Instead, they learn through interactions with their environments from an egocentric perception similar to humans. Consequently, there has been substantial growth in the demand for embodied AI simulators to support various embodied AI research tasks. This growing interest in embodied AI is beneficial to the greater pursuit of Artificial General Intelligence (AGI), but there has not be…
College of Electronic and Information Engineering, Nanjing University of Aeronautics and Astronautics（南京航空航天大学电子与信息工程学院）；College of Artificial Intelligence, Nanjing University of Aeronautics and Astronautics（南京航空航天大学人工智能学院）
Facebook AI Research（Facebook 人工智能研究院）；Georgia Institute of Technology（佐治亚理工学院）；Allen Institute for Artificial Intelligence（艾伦人工智能研究所）；Simon Fraser University（西蒙弗雷泽大学）；Robotics at Google（谷歌机器人部门）
We revisit the problem of Object-Goal Navigation (ObjectNav). In its simplest form, ObjectNav is defined as the task of navigating to an object, specified by its label, in an unexplored environment. In particular, the agent is initialized at a random location and pose in an environment and asked to find an instance of an object category, e.g., find a chair, by navigating to it. As the community begins to show increased interest in semantic goal specification for navigation tasks, a number of different often-inconsistent interpretations of this task are emerging. This document summarizes the co…
HMR-1: Hierarchical Massage Robot with Vision-Language-Model for Embodied Healthcare
The State Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation, Chinese Academy of Sciences, China（多模态人工智能系统国家重点实验室，自动化研究所，中国科学院）；Spatiotemporal AI, China（时空人工智能，中国）；Hangzhou International Innovation Institute, Beihang University, China（杭州国际创新研究院，北航，中国）；Georgia Institute of Technology, China（佐治亚理工学院，中国）；Key Laboratory of Computing Power Network and Information Security, Ministry of Education（计算功率网络与信息安全重点实验室，教育部；山东省计算机科学中心，齐鲁工业大学（山东省科学院），中国）；Shandong Computer Science Center, Qilu University of Technology (Shandong Academy of Sciences), China
UAVs Meet Embodied Intelligence: Bridging Human Intents and Flying Dynamics Via Harnessing Physical-Digital AI Agents
Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；Beihang University（北京航空航天大学）；Shanxi Datong University（山西大同大学）；Johns Hopkins University（约翰斯·霍普金斯大学）；University of Sanya（三亚学院）；Wenzhou-Kean University（温州肯恩大学）；Macau University of Science and Technology（澳门科技大学）；KOKONI 3D, Moxin Technology（魔芯科技KOKONI 3D）；University of Science and Technology of China（中国科学技术大学）
Towards Embodied Agentic AI: Review and Classification of LLM- and VLM-Driven Robot Autonomy and Interaction
Department of Computing, University of Turku（图尔库大学计算机系）；Institute of Computer Science, Zurich University of Applied Sciences（应用科学大学计算机科学研究所）；Centre for Artificial Ingelligence, Zurich University of Applied Sciences（应用科学大学人工智能中心）；Agentic Systems Lab, Department of Management, Technology and Economics, ETH Zürich（苏黎世联邦理工学院管理、科技与经济系代理系统实验室）；Faculty of Mathematics and Information Science, Warsaw University of Technology（华沙技术大学数学与信息科学学院）
EmbodiedMidtrain: Bridging the Gap between Vision-Language Models and Vision-Language-Action Models via Mid-training
Language Technologies Institute, Carnegie Mellon University（卡内基梅隆大学语言技术研究所）；Bosch Research North America & Bosch Center for Artificial Intelligence (BCAI)（博世北美研究部及博世人工智能中心（BCAI））
Recent years have seen an increasing amount of work on embodied AI agents that can perform tasks by following human language instructions. However, most of these agents are reactive, meaning that they simply learn and imitate behaviors encountered in the training data. These reactive agents are insufficient for long-horizon complex tasks. To address this limitation, we propose a neuro-symbolic deliberative agent that, while following language instructions, proactively applies reasoning and planning based on its neural and symbolic representations acquired from past experience (e.g., natural la…
University of Rochester（罗切斯特大学）；Massachusetts Institute of Technology（麻省理工学院）；Harvard University（哈佛大学）
Artificial intelligence (AI) literacy is a rapidly growing research area and a critical addition to K-12 education. However, support for designing tools and curriculum to teach K-12 AI literacy is still limited. There is a need for additional interdisciplinary human-computer interaction and education research investigating (1) how general AI literacy is currently implemented in learning experiences and (2) what additional guidelines are required to teach AI literacy in specifically K-12 learning contexts. In this paper, we analyze a collection of K-12 AI and education literature to show how co…
Artificial Intelligence (AI) plays varying roles in supporting both existing and emerging technologies. In the area of Learning and Tutoring, it plays key role in Intelligent Tutoring Systems (ITS). The fusion of ITS with Adaptive Hypermedia and Multimedia (AHAM) form the backbone of Adaptive eLearning Systems (AES) which provides personalized experiences to learners. This experience is important because it facilitates the accurate delivery of the learning modules in specific to the learner capacity and readiness. AES types vary, with Adaptive Web Based eLearning Systems (AWBES) being the popu…
