MEKAWIRE
研究

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。

3 篇论文
主题全部操作 76VLA 65导航 35机器人综合 32机器人学习 27感知 22人形机器人 21仿真 15运动控制 13安全 11World Action Models 4World-action model 3Aerial 2Aerial 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 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 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 1Federated 3D Perception 1Fisheye detection 1Floorplan SLAM 1Geometry-aware manipulation 1Grasping 1Grounding 1Humanoid Control 1Humanoid RL 1Humanoid Tool Use 1Humanoid loco-manipulation 1Humanoid safety 1Humanoid whole-body control 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 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 models 1World-Action Models 1
难度不限入门进阶专业
按技术筛选：仿真到现实迁移，清除
arXiv
Cross-embodiment transfer of humanoid whole-body controllers专业

TRACE：人形机器人全身控制模型的跨本体高效迁移

Qingyu Lan, Zhihao Tian, Zifan Wang, Junwei LiangThe Hong Kong University of Science and Technology (Guangzhou); The Hong Kong University of Science and Technology

香港科技大学团队在预印本中称，他们用一块GPU、约8小时，把为身高1.32米的宇树G1训练的全身控制模型SONIC迁移到1.82米的全尺寸宇树H2上，而SONIC本身训练耗费约2.1万GPU小时。若在更多机型上复现，人形机器人厂商换代时无需从零重训运动控制器。

RL仿真到现实迁移遥操作机器人基础模型
HumanoidFoundationUnitree RoboticsNVIDIA
阅读论文全文（PDF）
arXiv
Sim-to-Real Manipulation专业

RoboBridge：自进化具身智能体框架，用可修订技能实现仿真到真实迁移

Chenxi Li, Zhangrui Zhao, Rui Li, Yuan Gao, Kehui Liu, Jiarui Li, Dong Wang, Tong Si, Minting Pan, Wanli Ouyang, Dongzhan Zhou

RoboBridge 是一个自进化具身智能体框架，把仿真到真实迁移看作可执行任务技能的持续适应。它把预训练 VLA 策略作为可调用动作工具，结合推理时引导和反馈驱动的技能增删改验证，在 LIBERO-PRO 上总体成功率 74.4%，超过最强基线 Harness VLA 的 72.1%；在三个真实机器人任务上，仿真技能直接迁移后经真实反馈进化，成功率分别达到 15/15、14/15、15/15。该论文为预印本，尚未经过同行评审。

仿真到现实迁移
阅读论文全文（PDF）
arXiv
Humanoid whole-body control专业

CrossBFM：在不同人形机器人本体之间蒸馏共享的潜在行为空间

Tan-Dzung Do, Tuan Dat Phuong, Nico Bohlinger, Cuc T. Trinh, Siwei Ju, Vien Anh Ngo, Jan Peters, Xinchao Wang, An T. LeVinRobotics, National University of Singapore, TU Darmstadt, VinUniversity, DFKI, Hessian AI

能用一个提示让人形机器人模仿舞蹈、摆出姿态或追逐奖励的全身控制模型，每台机器人通常要花100多个GPU小时训练。VinRobotics、新加坡国立大学和德国达姆施塔特工业大学的这篇预印本，把为宇树G1训练的模型迁移到加速进化（Booster）T1、傅利叶N1和团队自研的M3上，只需不到1小时的共享编码器训练加每台不到10个GPU小时，跟踪精度与专用控制器相差无几。

机器人基础模型RL仿真到现实迁移
Unitree RoboticsBooster RoboticsFourier
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。