When humans perform everyday tasks, we naturally adjust our actions based on the current state of the environment. For instance, if we intend to put something into a drawer but notice it is closed, we open it first. However, many autonomous robots lack this adaptive awareness. They often follow pre-planned actions that may overlook subtle yet critical changes in the scene, which can result in actions being executed under outdated assumptions and eventual failure. While replanning is critical for robust autonomy, most existing methods respond only after failures occur, when recovery may be inefficient or infeasible. While proactive replanning holds promise for preventing failures in advance, current solutions often rely on manually designed rules and extensive supervision. In this work, we present a proactive replanning framework that detects and corrects failures at subtask boundaries by comparing scene graphs constructed from current RGB-D observations against reference graphs extracted from successful demonstrations. When the current scene fails to align with reference trajectories, a lightweight reasoning module is activated to diagnose the mismatch and adjust the plan. Experiments in the AI2-THOR simulator demonstrate that our approach detects semantic and spatial mismatches before execution failures occur, significantly improving task success and robustness.
NormAct: Benchmarking Embodied Agents' Proactive Compliance with Unspoken Social Norms
State Key Laboratory of General Artificial Intelligence, Beijing Institute for General Artificial Intelligence (BIGAI)（通用人工智能国家重点实验室，北京通用人工智能研究院）；China Academy of Information and Communications Technology（中国信息通信研究院）；ShanghaiTech University（上海科技大学）
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）
RoboSafe: Safeguarding Embodied Agents via Executable Safety Logic
Beihang University（北航大学）；AI Security Lab（360AI安全实验室）；The University of Sydney（悉尼大学）；Nanyang Technological University（南洋理工大学）；Peking University（北京大学）；Beijing Academy of Artificial Intelligence（北京人工智能研究院）
ContextFlow: Hierarchical Task-State Alignment for Long-Horizon Embodied Agents
Department of Electronic Engineering, Tsinghua University（清华大学电子工程系）；Qiuzhen College, Tsinghua University（清华大学启祯学院）；Beijing Institute of Mathematical Sciences and Applications（北京数学科学研究院）；Department of Data Science and Analytics Thrust, The Hong Kong University of Science and Technology (Guangzhou)（香港科学与技术大学（广州）数据科学与分析部门）；Institute of Microelectronics, Chinese Academy of Sciences（中国科学院微电子研究所）；University of Chinese Academy of Sciences（中国科学院大学）
6G Communication Networks Enabling Embodied Agents: Architecture and Prototype
State Key Laboratory of Novel Software Technology, Nanjing University（南京大学新型软件技术国家重点实验室）；Institute of Intelligent Networks and Communications (NINE), Nanjing University (Suzhou Campus)（南京大学智能网络与通信研究所（苏州校区））
Towards Real-time Adaptation of Embodied Agent in Human-Robot Collaboration
University of Southern California Department of Electrical（南加州大学电气与计算机工程系）；University of Southern California Department of Computer Science Los Angeles CA USA（南加州大学计算机科学系洛杉矶加州美国）；University of Southern California（南加州大学）
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…
GraphThink: Graph-Enhanced LLM Thinking for Long-Horizon Embodied Task Planning
Gaoling School of Artificial Intelligence, Renmin University of China（中国人民大学高瓴人工智能学院）；Beijing Academy of Artificial Intelligence（北京人工智能研究院）；Tsinghua University（清华大学）；The Hong Kong Polytechnic University（香港理工大学）
RobotEQ: Towards Social Proactive Intelligence in Embodied Agents
State Key Laboratory of Autonomous Intelligent Unmanned Systems, Tongji University（自主智能无人系统国家重点实验室，同济大学）；Tsinghua University（清华大学）；The Chinese University of Hong Kong（香港中文大学）；CMVS, University of Oulu（奥卢大学CMVS）
DreamFly: Causal Memory and Receding-Horizon Diffusion Planning for Aerial Vision-Language Navigation
School of Electronic Information Engineering, Xi’an Technological University（西安工业大学电子信息工程学院）；School of Computer Science and Engineering, Xi’an Technological University（西安工业大学计算机科学与工程学院）
The paper focuses on collision-inclusive motion planning for impact-resilient mobile robots. We propose a new deformation recovery and replanning strategy to handle collisions that may occur at run-time. Contrary to collision avoidance methods that generate trajectories only in conservative local space or require collision checking that has high computational cost, our method directly generates (local) trajectories with imposing only waypoint constraints. If a collision occurs, our method then estimates the post-impact state and computes from there an intermediate waypoint to recover from the …
Realtime-VLA FLASH: Speculative Inference Framework for Diffusion-based VLAs
State Key Lab of Processors, Institute of Computing Technology, CAS（处理器国家重点实验室，计算技术研究所，中国科学院）；University of Chinese Academy of Sciences（中国科学院大学）；Nanjing University（南京大学）；Dexmal
Embodied Hazard Mitigation using Vision-Language Models for Autonomous Mobile Robots
Department of Mechanical and Aerospace Engineering, Tandon School of Engineering, New York University（机械与航空航天工程系，坦顿工程学院，纽约大学）；GenAuto.ai by General Autonomy Inc.（General Autonomy Inc. 的 GenAuto.ai）
A Formal Resilience Framework for Cyber-Physical Embodied Systems under Device-Level Cyberattacks
Department of Computer Science, Örebro University, Sweden（瑞典 Örebro 大学计算机科学系）；AI, Robotics and Cybersecurity Center (ARC), Örebro University, Sweden（瑞典 Örebro 大学人工智能、机器人与网络安全中心）
Transforming Monolithic Foundation Models into Embodied Multi-Agent Architectures for Human-Robot Collaboration
Department of Computer Science and Technology, Tsinghua University（计算机科学与技术系，清华大学）；School of Artificial Intelligence, Beijing University of Posts and Telecommunications（人工智能学院，北京邮电大学）
UniTrackPLA: Unified Panorama-Language-Action Model for Instruction-Guided Navigation and Dynamic Person Tracking
College of Integrated Circuits, Hunan University（湖南大学集成电路学院）；National Engineering Research Center of Robot Visual Perception and Control Technology, Hunan University（湖南大学机器人视觉感知与控制技术国家工程研究中心）；School of Advanced Technology, Xi’an Jiaotong-Liverpool University（西交利物浦大学智能工程学院）；Suzhou VSDeep Intelligent Technology Co., Ltd.（苏州维深智能科技有限公司）
