Natural language guided embodied task completion is a challenging problem since it requires understanding natural language instructions, aligning them with egocentric visual observations, and choosing appropriate actions to execute in the environment to produce desired changes. We experiment with augmenting a transformer model for this task with modules that effectively utilize a wider field of view and learn to choose whether the next step requires a navigation or manipulation action. We observed that the proposed modules resulted in improved, and in fact state-of-the-art performance on an unseen validation set of a popular benchmark dataset, ALFRED. However, our best model selected using the unseen validation set underperforms on the unseen test split of ALFRED, indicating that performance on the unseen validation set may not in itself be a sufficient indicator of whether model improvements generalize to unseen test sets. We highlight this result as we believe it may be a wider phenomenon in machine learning tasks but primarily noticeable only in benchmarks that limit evaluations on test splits, and highlights the need to modify benchmark design to better account for variance in model performance.
EmbodiedCLBench: Evaluating Continual Learning for Self-Evolving Embodied Agents
School of Intelligence Science and Technology, Peking University（北京大学智能科学与技术学院）；State Key Laboratory of General Artificial Intelligence, BIGAI（北京通用人工智能研究院通用人工智能国家重点实验室）；University of Hong Kong（香港大学）
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）
EARBench: Towards Evaluating Physical Risk Awareness for Task Planning of Foundation Model-based Embodied AI Agents
School of Data Science, The Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳）数据科学学院）；AI Lab, Tencent（腾讯AI实验室）；Robotics X, Tencent（腾讯Robotics X实验室）；Nanjing University of Posts and Telecommunications（南京邮电大学）
Self-Evolving Cognitive Framework via Causal World Modeling for Embodied Scientific Intelligence
Graduate School of Advanced Science and Engineering, Hiroshima University（广岛大学先进科学与工程研究生院）；Advanced Intelligence and Robotics Research Center, Brain Science Institute, Tamagawa University（玉川大学脑科学研究所先进智能与机器人研究中心）
A Survey of Embodied AI: From Simulators to Research Tasks
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）
GuardianBench: A Same-Scene Instruction-Contrastive Benchmark for Latent Contextual Risk in Embodied AI
Shandong University（山东大学）；National University of Singapore（新加坡国立大学）；Nanjing University of Aeronautics and Astronautics（南京航空航天大学）；Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；Xiaomi Corporation（小米公司）
FluxVLA Engine: A One-Stop VLA Engineering Platform for Embodied Intelligence
LimX Dynamics；Nankai University（南开大学）；The University of Hong Kong（香港大学）；University of Electronic Science and Technology of China（电子科技大学）；Beijing Institute of Technology（北京理工大学）；Xidian University（西安电子科技大学）；Zhejiang University（浙江大学）；Xi’an Jiaotong University（西安交通大学）
Bridging Values and Behavior: A Hierarchical Framework for Proactive Embodied Agents
State Key Laboratory of General Artificial Intelligence, BIGAI（通用人工智能国家重点实验室，BIGAI）；School of Computer Science, Peking University（北京大学计算机学院）；Tsinghua University（清华大学）；Nat’l Eng. Research Center of Visual Technology, Peking University（北京大学视觉技术国家工程研究中心）；Institute for AI, Peking University（北京大学人工智能研究院）；State Key Laboratory of General Artificial Intelligence, Peking University（通用人工智能国家重点实验室，北京大学）
SpaceMind: A Modular and Self-Evolving Embodied Vision-Language Agent Framework for Autonomous On-orbit Servicing
University of Chinese Academy of Sciences（中国科学院大学）；Technology and Engineering Center for Space Utilization, Chinese Academy of Sciences（中国科学院空间利用技术与工程中心）
SoMi-ToM: Evaluating Multi-Perspective Theory of Mind in Embodied Social Interactions
The University of Hong Kong（香港大学）；Carnegie Mellon University（卡内基梅隆大学）；Johns Hopkins University（约翰霍普金斯大学）；University of California Irvine（加州大学尔湾分校）；Stanford University（斯坦福大学）
IGLU Gridworld: Simple and Fast Environment for Embodied Dialog Agents
Moscow Institute of Physics and Technology（莫斯科物理技术学院）；Artificial Intelligence Research Institute（人工智能研究所）；ITMO University（圣光机大学）；Microsoft Research（微软研究院）；Meta AI
VidEgoThink: Assessing Egocentric Video Understanding Capabilities for Embodied AI
Tsinghua University（清华大学）；Institute for AI Industry Research (AIR), Tsinghua University（清华大学人工智能产业研究院（AIR））；Tencent Robotics X（腾讯Robotics X实验室）；University of Toronto（多伦多大学）；Zhili College, Tsinghua University（清华大学志道书院）
Growing Perspectives: Modelling Embodied Perspective Taking and Inner Narrative Development Using Large Language Models
Department of Psychology, University of Milan-Bicocca（心理学系，米兰-比科卡大学）；Department of Psychology, University of Essex（心理学系，埃塞克斯大学）；School of Social Sciences and Technology, Technical University of Munich（社会科学与技术学院，慕尼黑技术大学）；Department of Electrical Engineering and Information Technology, University of Naples Federico II（电气工程与信息技术系，那不勒斯费德里科二世大学）
EMBGuard: Constructing Hazard-Aware Guardrails for Safe Planning in Embodied Agents
Independent Researcher（独立研究者）；Department of Biomedical Engineering（生物医学工程系）；the Department of Intelligent Precision Healthcare Convergence, Sungkyunkwan University（智能精准医疗融合系，全州大学）；Department of Artificial Intelligence, Yonsei University（人工智能系，延世大学）
