LAPT-Scale: Scaling Embodied Policies with Latent Action Pre-Training on Human Videos
Joy Future Academy, JD（京东探索研究院）；School of Computer Science and Technology, Harbin Institute of Technology, Shenzhen（哈尔滨工业大学（深圳）计算机科学与技术学院）；Tsinghua SIGS, Tsinghua University（清华大学深圳国际研究生院）；Artificial Intelligence, The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州）人工智能学院）
Scaling generalist robotic policies is limited by the high cost of collecting embodied interaction data. Large-scale egocentric human videos offer a rich alternative, but they lack robot action labels, and existing action annotation methods are often noisy under real-world visual complexity and diverse manipulation dynamics. We introduce LAPT-Scale, a scalable latent action pre-training paradigm with two components: LAPT-Scale-LAM learns continuous latent actions by explaining frame-to-frame visual transitions in human videos and uses them as human-derived supervision, while LAPT-Scale-Policy trains a VLA policy through human-video latent-action pre-training, target-robot embodiment alignment, and task-specific post-training. Unlike prior vector-quantized LAMs, LAPT-Scale-LAM uses a continuous latent space to preserve fine-grained motion and interaction dynamics. We further combine perceptual, semantic, and optical-flow alignment signals to improve transition-level supervision beyond pixel reconstruction. Trained on 23.3K hours of egocentric video, LAPT-Scale-LAM provides implicit action supervision for policy pre-training, yielding consistent scaling gains and improving real-robot success from 63.3% to 85.0% as annotated human video scales from 1K to 5K hours. The learned policy also generalizes to unseen visual conditions.
PACT: Self-Evolving Physical Safety Alignment for Diffusion Policies in Embodied Manipulation
Dept. of Comp. Sci. and Tech., Institute for AI, Tsinghua-Bosch Joint ML Center, THBI Lab, BNRist Center, Tsinghua University, Beijing, 100084, China（计算机科学与技术系，人工智能研究院，清华-博世联合机器学习中心，THBI实验室，BNRist中心，清华大学，北京，100084，中国）；Peng Cheng Laboratory, 518108, China（鹏城实验室，518108，中国）
EmbodiedGovBench: A Benchmark for Governance, Recovery, and Upgrade Safety in Embodied Agent Systems
School of Software, Harbin Institute of Technology（哈尔滨工业大学软件学院）；School of Computer Science and Technology, Harbin Institute of Technology（哈尔滨工业大学计算机科学与技术学院）；School of Mathematical and Computer Sciences, Heriot-Watt University, Malaysia Campus（赫瑞-瓦特大学马来西亚校区数学与计算机科学学院）；School of Future Science and Engineering, Soochow University（苏州大学未来科学与工程学院）
EmbodiedSkills: A Unified Framework for Orchestrating, Training, and Deploying VLA Agents
College of Computer Science and Technology, Zhejiang University（浙江大学计算机科学与技术学院）；Nanjing University of Aeronautics and Astronautics（南京航空航天大学）；Cornell University（康奈尔大学）；Universal Ubiquitous AI Co., Ltd.（Universal Ubiquitous AI有限公司）；Hangzhou DEEP Robotics Technology Co., Ltd.（杭州深度机器人科技有限公司）；National University of Singapore（新加坡国立大学）
Meta AI Research（Meta人工智能研究院）；Carnegie Mellon University（卡内基梅隆大学）；University of Illinois at Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）
Realistic long-horizon tasks like image-goal navigation involve exploratory and exploitative phases. Assigned with an image of the goal, an embodied agent must explore to discover the goal, i.e., search efficiently using learned priors. Once the goal is discovered, the agent must accurately calibrate the last-mile of navigation to the goal. As with any robust system, switches between exploratory goal discovery and exploitative last-mile navigation enable better recovery from errors. Following these intuitive guide rails, we propose SLING to improve the performance of existing image-goal naviga…
Embodied Foundation Models at the Edge: A Survey of Deployment Constraints and Mitigation Strategies
University of South Florida（佛罗里达州立大学）；Florida International University（佛罗里达国际大学）；Johns Hopkins University（约翰霍普金斯大学）；Nottingham Trent University（诺丁汉特伦特大学）；Imperial College London（伦敦帝国理工学院）
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）
Guide, Think, Act: Interactive Embodied Reasoning in Vision-Language-Action Models
Futian Laboratory（福田实验室）；Faculty of Computing, Harbin Institute of Technology（哈尔滨工业大学计算机学院）；International Digital Economy Academy (IDEA)（国际数字经济学院（IDEA））；School of Robotics, Hunan University（湖南大学机器人学院）；South China University of Technology（华南理工大学）；Visincept（Visincept公司）；National Key Laboratory of Smart Farm Technologies and Systems（智能农业技术与系统国家重点实验室）
Towards Deploying VLA without Fine-Tuning: Plug-and-Play Inference-Time VLA Policy Steering via Embodied Evolutionary Diffusion
Collaborative and Versatile Robots (CLOVER) Laboratory, T-Stone Robotics Institute, The Chinese University of Hong Kong, Hong Kong（协作与多功能机器人实验室，T-Stone机器人研究所，香港中文大学，香港）；Φ \Phi -Institute for Physical Human Intelligence（物理人机智能研究所）；Center for Embodied Artificial Intelligence and Computer Vision, Shenzhen Loop Area Institute, Shenzhen, China（具身人工智能与计算机视觉中心，深圳环园研究院，深圳，中国）；Department of Advanced Robotics, Istituto Italiano di Tecnologia, Genoa, Italy（先进机器人系，意大利理工学院，热那亚，意大利）
EmbodiedSWE: Coding Agents for Long Horizon Dexterous Robotics
ByteDance Seed（字节跳动）；Yale University（耶鲁大学）；Princeton University（普林斯顿大学）；Carnegie Mellon University（卡内基梅隆大学）；Stanford University（斯坦福大学）；University of California, Los Angeles（加州大学洛杉矶分校）；University of Washington（华盛顿大学）
Learning Primitive Embodied World Models: Towards Scalable Robotic Learning
Shanghai AI Lab（上海人工智能实验室）；Fudan（复旦大学）；SJTU（上海交通大学）；NJUST（南京理工大学）；THU（清华大学）；Harvard（哈佛大学）；ZJU（浙江大学）；NJU（南京大学）；USTC（中国科学技术大学）；Tongji（同济大学）；HKUST (GZ)（香港科技大学（广州））
Intelligent Automation for Embodied Benchmark Construction: Pipelines, Embodiments, Simulators, and Trends
University of Electronic Science and Technology of China（电子科技大学）；Qiyuan Lab（启元实验室）；Beijing University of Posts and Telecommunications（北京邮电大学）；Tsinghua University（清华大学）；Beihang University（北京航空航天大学）
University of Illinois, Urbana-Champaign（伊利诺伊大学厄巴纳-尚佩恩分校）；Georgia Institute of Technology（佐治亚理工学院）；Facebook AI Research（Facebook AI研究院）
It is fundamental for personal robots to reliably navigate to a specified goal. To study this task, PointGoal navigation has been introduced in simulated Embodied AI environments. Recent advances solve this PointGoal navigation task with near-perfect accuracy (99.6% success) in photo-realistically simulated environments, assuming noiseless egocentric vision, noiseless actuation, and most importantly, perfect localization. However, under realistic noise models for visual sensors and actuation, and without access to a "GPS and Compass sensor," the 99.6%-success agents for PointGoal navigation on…
Microsoft Research（微软研究院）；Carnegie Mellon University（卡内基梅隆大学）；University of Washington（华盛顿大学）
Given a simple request like Put a washed apple in the kitchen fridge, humans can reason in purely abstract terms by imagining action sequences and scoring their likelihood of success, prototypicality, and efficiency, all without moving a muscle. Once we see the kitchen in question, we can update our abstract plans to fit the scene. Embodied agents require the same abilities, but existing work does not yet provide the infrastructure necessary for both reasoning abstractly and executing concretely. We address this limitation by introducing ALFWorld, a simulator that enables agents to learn abstr…
Inspect Robots: Evaluating the Capabilities and Safety of Embodied AI
Robocurve；University of Southern California（南加州大学）；San Jose State University（圣何塞州立大学）；Indian Institute of Technology Bhilai（印度理工学院比莱校区）；University of Maryland（马里兰大学）；Stony Brook University（石溪大学）；Arizona State University（亚利桑那州立大学）；Brown University（布朗大学）
Mind and Motion Aligned: A Joint Evaluation IsaacSim Benchmark for Task Planning and Low-Level Policies in Mobile Manipulation
AIRI；Moscow Institute of Physics and Technology (MIPT)（莫斯科物理技术学院）；Lomonosov Moscow State University（罗蒙诺索夫莫斯科国立大学）；Sberbank, Robotics Center（Sberbank机器人中心）；Skoltech
本文提出 Kitchen-R 基准，在 Isaac Sim 数字孪生厨房环境中统一评估任务规划与底层控制，并提供视觉语言模型规划、扩散策略控制及轨迹采集系统，支持独立与集成评估，弥补具身 AI 中语言引导机器人整体评测的空白。
