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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
主题全部操作 96VLA 68导航 52机器人综合 41机器人学习 32仿真 24感知 23人形机器人 20运动控制 16安全 13Aerial 4World Action Models 4World-action model 3Aerial manipulation 2Dexterous manipulation 2Sim-to-Real Manipulation 2Social Navigation 2Vision-Language Navigation 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 1Benchmark for adding wrist force sensing to VLA manipulation policies 1Bimanual Manipulation 1Byzantine localization 1Canal inspection 1Co-speech Humanoid 1Compositional Manipulation 1Construction navigation 1Contact-Rich Manipulation 1Contact-rich Manipulation 1Continual reinforcement learning on top of robot foundation models 1Continuum sensing 1Controlled comparison of how to add world models to VLA robot policies 1Cross-embodiment transfer of humanoid whole-body controllers 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 1Evaluating robot world models against real executions 1Event-camera VO 1Exoskeletons 1Failure recovery 1Faster on-device inference for flow-matching vision-language-action models 1Federated 3D Perception 1Fisheye detection 1Floorplan SLAM 1Force-aware robot foundation model pretrained on 2,000 hours of human tactile-glove data 1Geometry-aware manipulation 1Grasping 1Grounding 1Humanoid Control 1Humanoid RL 1Humanoid Tool Use 1Humanoid loco-manipulation 1Humanoid safety 1Humanoid whole-body control 1Humanoid whole-body training data from robot-specific video generation 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 Optimization 1Preference Learning 1Quadruped locomotion 1Real-time VLA 1Real-to-sim 1Reinforcement learning for industrial assembly 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 Exploration 1Tactile VLA 1Temporal manipulation 1Terrain traversability 1Text-to-3D Policy 1Tool Co-Design 1Trajectory Correction 1Traversability 1Turning phone-recorded human demonstrations into per-robot post-training data by retargeting object motion 1Two Unitree G1 humanoids playing table-tennis doubles with learned collision-risk filtering 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 1Vision-language-action models without wrist cameras (learned foveation) 1Visual Generalization 1Visual Grounding 1Visual Reward Models 1Visuomotor Fusion 1Visuotactile manipulation 1World Models 1World models 1World-Action Models 1compliant humanoid whole-body control 1data engine converting egocentric human video into replayable bimanual dexterous robot demonstrations 1dexterous in-hand manipulation with learned world models 1efficient inference for vision-language-action robot policies 1humanoid learning from human video 1humanoid teleoperation with heavy unknown payloads 1humanoid whole-body manipulation 1large-scale tactile demonstration dataset and robot-free data collection 1open vision-language-action model with register-token memory 1post-training vision-language-action policies for industrial disassembly 1real-world reinforcement learning for robot policies 1robot foundation model training 1robot learning from human video 1tactile pretraining for vision-language-action models 1terrain-aware behavioral foundation model for humanoid whole-body control 1training-signal allocation for vision-language-action robot policies 1vision-language-action models 1world models for dexterous manipulation 1
难度不限入门进阶专业
暂无中文版，显示英文原文。
按技术筛选：合成数据生成，清除
arXiv
world models for dexterous manipulation专业

感知、预测与规划合一：面向第一视角灵巧操作的统一世界动作模型P3-WAM

Zirui Wu, Chen Guo, Tianjian Jiang, Zeren Jiang, Zeming Li, Lingchen Yang, Longteng Duan, Ying Xue, Handi Yin, Shenyuan Gao, Martin R. Oswald, Jun Zhang, Ping Tan, Jie SongHKUST (Guangzhou), HKUST, Primatix, ETH Zurich, University of Oxford (VGG), University of St. Gallen

这篇预印本构建了单一视频模型P3-WAM，既能从第一视角视频中还原三维手部动作，也能预测给定手部动作后的场景，还能仅凭文字指令同时规划动作及其结果视频。三种能力联合训练后各自均有提升，并在四个公开数据集上超过专用模型，但机器人应用目前仅有两个开环演示。

灵巧操作合成数据生成世界模型
NVIDIA
阅读论文全文（PDF）
arXiv
humanoid learning from human video专业

KODA：将人类第一视角演示转化为人形机器人全身训练数据的运动学与观测对齐方法

Zibo Zhou, Yueru Chen, Junsong Wu, Weiji Xie, Qingyao Xu, Sheng Yin, Shiqing Wang, Jiyuan Shi, Siheng Chen, Chenjia Bai, Xuelong LiTeleAI (China Telecom), Shanghai Jiao Tong University, Gamma Robotics

这篇预印本把人们用胸前iPhone拍摄的视频转换成宇树G1人形机器人可学习的数据，并合成机器人自身相机应看到的画面。每个任务在50条机器人演示之外加入100条转换后的人类演示，在未见过的物体条件下成功率提高约45个百分点。

模仿学习VLA合成数据生成仿真到现实迁移
HumanoidUnitree RoboticsNVIDIA
阅读论文全文（PDF）
arXiv
Turning phone-recorded human demonstrations into per-robot post-training data by retargeting object motion专业

迁移物体而非手：把手机拍摄的人类示范变成任意机器人的后训练数据

Nagasrikanth KallakuriDxtr AI Co.

初创公司Dxtr AI的单作者预印本把手机拍摄的人类示范转化为只记录物体运动的数据，再在仿真中判定8种机器人中哪些能复现：157个机器人与示范组合中31.2%通过，52.2%失败，14.0%被事先拒绝。

模仿学习灵巧操作VLA合成数据生成仿真到现实迁移
阅读论文全文（PDF）
arXiv
Humanoid whole-body training data from robot-specific video generation专业

KinoGen：用机器人自身视频生成人形机器人全身操作参考动作

Qi Wu, Lulu Zhao, Sanghyun Hahn, Rishi Yennu, Emmy Yang, Dingqi Zhang, Tao Pang, Jiuguang Wang, Kuan FangCornell University; Multiply Labs

康奈尔大学与Multiply Labs的预印本论文，让视频生成模型直接"想象"人形机器人自己完成任务，再把真实的机器人和物体模型拟合到视频上，得到训练用参考动作。基于这些动作训练的策略在仿真中成功率57%，高于基于人类视频的最佳方案的42%，四项任务已在真实宇树G1上运行。

灵巧操作合成数据生成仿真到现实迁移RL
HumanoidUnitree RoboticsMultiply LabsNVIDIA
阅读论文全文（PDF）
arXiv
Navigation专业

NavAble: A Large-Scale Dataset and Synthetic Data Generation Pipeline for Blind Navigation

Hochul Hwang, Jahir Sadik Monon, Soowan Yang, Shiven Umeshbhai Patel, Anh Nhat Hong Nguyen, Kien Nguyen, Keshav Garg, Dylan Gage, Duretti Hordofaa, Anshu Anjna, Eshed Ohn-Bar, Donghyun Kim

Reliable recognition of accessibility-critical objects (e.g., audible pedestrian signals, door-activation buttons, and handrails) is essential for assistive navigation technologies that support safe, independent mobility for blind and low-vision (BLV) users. Yet these categories are underrepresented in existing large-scale vision datasets, and the few datasets that include them suffer from limited class coverage, inconsistent annotations, and poor diversity.

合成数据生成
阅读论文全文（PDF）

主题页

操作 8VLA 4World Action Models 5World-action model 3Aerial 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。