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论文

机器人与具身智能领域的最新论文，以及每篇对产业的意义。

5 篇论文
主题全部Manipulation 65VLA 43Navigation 24Learning 23Robotics 22Perception 20Humanoid 15Safety 9Simulation 9Locomotion 8World Action Models 4Aerial 2Aerial manipulation 2Dexterous manipulation 2Sim-to-Real Manipulation 2Social Navigation 2Vision-Language Navigation 2World models 2World-action model 23D Assembly 13DGS dataset 15G Simulation 1AMR Operations 1Active vision manipulation 1Adaptive MPC 1Adaptive action horizons 1Adaptive intervention 1Aerial Manipulation 1Aerial navigation 1Agricultural Robotics 1Articulation Estimation 1Autonomous Driving 1Autonomous Exploration 1Battery-aware RL 1Belief-space planning 1Bimanual Manipulation 1Byzantine localization 1Canal inspection 1Co-speech Humanoid 1Compositional Manipulation 1Construction navigation 1Contact-Rich Manipulation 1Contact-rich Manipulation 1Continuum sensing 1Curriculum RL 1Dexterous Manipulation 1Distributed Manipulation 1Distribution Shift 1Driving Simulation 1Efficient VIO 1Egocentric data 1Egocentric video 1Embodied Agents 1Embodied policy 1Empowerment MPC 1Endoscopic navigation 1Endovascular robotics 1Event-camera VO 1Exoskeletons 1Failure recovery 1Federated 3D Perception 1Fisheye detection 1Floorplan SLAM 1Geometry-aware manipulation 1Grasping 1Grounding 1Humanoid Control 1Humanoid RL 1Humanoid Tool Use 1Humanoid control 1Humanoid loco-manipulation 1Humanoid safety 1Imitation Learning 1JEPA World Models 1Lab automation 1Laboratory Manipulation 1Latent World Models 1Legged Manipulation 1LiDAR Security 1Lifelong RL 1MAPF 1MAV guidance 1Manipulation LfD 1Manipulation Safety 1Marine simulation 1Memory-centric VLA 1Miniature Locomotion 1Mining Perception 1Mobile manipulation 1Model-based RL 1Monocular SSC 1Motion Planning 1Multi-Agent RL 1Multi-Robot Exploration 1Multi-Robot Manipulation 1Multi-UAV Exploration 1Multi-UAV State Estimation 1Multi-agent RL 1Multi-agent motion planning 1Multi-camera odometry 1Multi-fidelity RL 1Multi-robot manipulation 1Multi-skill Manipulation 1Multi-view perception 1Multimodal simulation 1Navigation & Mapping 1Object Pose Estimation 1Object Search 1Panoramic 3D Detection 1Panoramic segmentation 1Pedestrian tracking 1PersonNav 1Planning 1Policy 1Policy Optimization 1Preference Learning 1Quadruped locomotion 1Real-time VLA 1Real-to-sim 1Representation alignment 1Robot-Use Agents 1Runtime Verification 1Safe Generative Planning 1Safe RL-MPC 1Safe VLA 1Safety monitoring 1Safety-critical control 1Sim-to-real Navigation 1Sim-to-real manipulation 1Simulation geometry 1Skill Discovery 1Social navigation 1Soft Pneumatic Actuators 1Soft manipulation 1Sonar SLAM 1Streaming 3D reconstruction 1Submovement decomposition 1Surgical 1Surgical robotics 1Tactile 1Tactile Exploration 1Tactile VLA 1Temporal manipulation 1Terrain traversability 1Text-to-3D Policy 1Tool Co-Design 1Trajectory Correction 1Traversability 1UAV Navigation 1UAV Swarms 1UAV traffic monitoring 1Underwater Robotics 1Underwater active mapping 1Underwater perception 1Unified world-action model 1VLA Conditioning 1VLA Defense 1VLA Robustness 1VLA evaluation 1VLA manipulation 1VLA robustness 1Video world models 1Visual Generalization 1Visual Grounding 1Visual Reward Models 1Visuomotor Fusion 1Visuotactile manipulation 1World Models 1World-Action Models 1
难度不限入门进阶专业
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arXiv
World models进阶

DeltaWorld：动作条件潜变量增量学习让机器人世界模型更物理一致

Boyuan Hou, Xiaoge Cao, Chaofan Zhang, Shuo Wang, Shaowei Cui

论文提出DeltaWorld，一种用于机器人操作的物理一致交互式世界模拟器。其Delta-LTM预测动作引起的潜变量增量并叠加到当前状态，Interaction-aware Latent Alignment强化机器人-物体交互区域的监督。在IWS基准和自我采集的跨机器人数据集上，DeltaWorld相比IWS基线将FVD降低46.6%，LPIPS降低31.1%。该研究为预印本，尚未经同行评审。

世界模型
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arXiv
Video world models进阶

PhysicsLENS 揭示视频生成模型“物理属性盲区”：看似合理实则忽略隐藏物理条件

Isaiah Milkey, Som Sagar, Aditya Taparia, Xinyuan Liu, Jiqing Wen, Ransalu Senanayake

PhysicsLENS 是一个新基准，通过匹配场景对评测机器人视频生成模型是否遵循文本指定的隐藏物理属性。在 4 个模型生成的 439 个视频中，47 个被人工评定为物理合理，其中 34 个（72%）未遵循指定属性；同一模型在隐藏属性条件下物理合理性平均下降 0.28 分，但不显著。该工作说明视觉合理性与属性遵循必须分开评估，对机器人世界模型可靠性有重要意义。本文为 arXiv 预印本，未经同行评审。

世界模型
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arXiv
Social Navigation进阶

Social-WM：用可执行动作差异做安全社交导航的隐世界模型

Zhihao Zheng, Mooi Choo Chuah

Social-WM 是一个使用 RGB 视频离线训练的隐世界模型规划框架，区分名义动作与实际可执行动作。在 Social-HM3D 上，它取得 63.77% 成功率，行人碰撞率降至 21.67%，比 NavThinker 相对降低 44.6%；零样本迁移到 Social-MP3D 仍保持 54.15% 成功率和 23.34% 碰撞率。该工作为未同行评审的预印本。

世界模型
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arXiv
Manipulation进阶

RoboCoach 让世界模型主动当教练：想象失败后定向补数据，真机成功率升至 75.0% 和 83.8%

Jiajun Liu, Yifan Chen, Yichao Liu, Jiayi Zhang, Ruoqu Chen, Shaoxuan Xie, Guocai Yao, Mengdi Xu, Sen Cui, Changshui Zhang

RoboCoach 是一个预印本框架，用世界模型中的想象执行来诊断长程机器人任务中最先失败的子任务，并据此请求对应技能专家的演示数据。在 Franka 和 AgileX 真机上，仅追加 150 条子任务演示，成功率分别从 13.3% 升至 75.0%、从 40.0% 升至 83.8%，显著超过统一采集加共享适配器的基线。该工作表明世界模型可以成为主动教练，把稀缺的真实数据导向可复用的技能模块。

世界模型
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主题页

操作 8VLA 4World Action Models 5Aerial manipulation 3Dexterous manipulation 3Sim-to-Real Manipulation 3Social Navigation 3

论文来自 arXiv 机器人领域。尚未生成摘要的论文，显示摘要开头部分。