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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。

5 篇论文
主题全部Manipulation 27VLA 22Perception 12Robotics 11Navigation 10Humanoid 9Learning 8Safety 5World Action Models 4Simulation 3Aerial 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 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 1Locomotion 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 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
arXiv
Manipulation进阶

桥接前沿推理与机器人执行：从自主演示生成到稠密语言监督

Bosung Kim, Alexander Trevithick, Ruiyi Wang, Prithviraj Ammanabrolu

这是一篇预印本论文，尚未经过同行评审。作者提出两条互补路径：让 GPT-6 Astra 等前沿模型自主生成演示数据并加入纠错片段，训练低延迟本地策略；同时用三层次嵌套语言标注和多方面指令描述训练策略，提升指令跟随能力。在 RoboCasa 365 和 BEHAVIOR-1K 上，层次化与多方面监督组合达到最高分，混合演示与纯人类演示的总体成功率相同，十字填词任务中超时预算内累计动作执行时间提升约 3.4 倍。

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arXiv
Manipulation进阶

AdaTempo：学习演示中的共享相对节奏，实现更快机器人操作

Jiale Cao, Yike Niu, Zhengrong Xue, Huazhe Xu

这篇预印本提出 AdaTempo，一种自监督方法，通过提取多个演示中共享的相对节奏模式，对演示轨迹进行离线重采样，训练出更快执行的视觉运动策略。在5个仿真和6个真机任务上，AdaTempo 最高实现3.57倍加速，仿真平均成功率达75.5%、平均加速2.83倍，真机汇总成功率从58.3%提升到77.8%且平均快2.02倍。该方法无需运行时速度选择或在线重定时，简化部署。

模仿学习
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arXiv
Manipulation进阶

PAM用本体感觉草图引导动作生成，真机双机械臂成功率升至75%

Fangyuan Wang, Songhao Huang, Haoxiang Sun, Shipeng Lyu, Chengyang He, Anqing Duan, Peng Zhou, David Navarro-Alarcon

这篇预印本提出Proprioceptive Action Models (PAM)，用弧长参数化的B样条草图表示机器人剩余关节路径，并与短动作块联合去噪生成。在Push-T、LIBERO-Long仿真和四个真机双臂任务中，PAM均优于纯动作基线；真机平均成功率从47.5%提高到75.0%。该方法为长程操作提供了紧凑且免时序的运动意图表示，但尚未经过同行评审。

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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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arXiv
Manipulation进阶

Encore：编码智能体用少量演示发现机械臂操作策略，LIBERO-PRO扰动集成功率96.3%

Yifan Kang, Zihan Wang, Zhiwen Fan, Bangya Liu

这篇arXiv预印本提出Encore框架，将遥操作演示蒸馏为编码智能体可查询的证据包，让智能体编写、运行并迭代修复策略程序，运行时不调用模型。在LIBERO-PRO的60个扰动单元上，3条演示的冻结程序在3000个密封回合中成功2889次（96.3%），无演示为2846次（94.9%）；在RoboDojo上无演示全部失败，3条演示在前10个任务取得95/450成功。双臂真机上完成手递手和杯子倒扣，各9/10成功。

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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。