Whether labelling messy human demonstrations by quality improves robot foundation models专业
质量感知VLA预训练中什么最重要：基准与实证研究
人类遥操作员难免犹豫、绕路，这篇预印本检验了按质量给这类数据打标签能否让机器人模型更好。结论是有效但幅度小且因模型而异，反倒是普通的仿真预训练把真机成功率从9.6%提升到31.7%。
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。
1 篇论文
主题全部操作 88VLA 57导航 52机器人综合 41机器人学习 32仿真 24感知 23运动控制 15人形机器人 14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 1A 100-hour wearable dataset of people walking and working with their hands, recorded as video, whole-body motion, finger motion and touch for humanoid training 1A 4-billion-parameter perception model that points at and boxes objects precisely, used as the visual backbone for robot manipulation and driving policies 1A residual correction layer that keeps the size, energy and posture of humanoid gestures such as waving, bowing and crouching when an existing whole-body tracker executes them 1A safety layer that decides, at every control step, whether a flipping humanoid should finish the move, abort and land on its feet, or fall in a way that protects its head and hands 1AMR Operations 1Active vision manipulation 1Adapting a frozen flow-matching VLA by learning only the noise it starts from 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 1Benchmark for few-shot skill transfer with dexterous robot hands and tactile sensing 1Bimanual Manipulation 1Black-box correction of deployed robot policies with world-model planning 1Byzantine localization 1Canal inspection 1CapPro lets a pi0.5 robot policy attempt a new manipulation task after watching one human video, without new robot demonstrations or fine-tuning 1Co-speech Humanoid 1Coding agents that control robots and keep what they learn as reusable skills and lessons 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 1Humanoid Control 1Humanoid RL 1Humanoid Tool Use 1Humanoid control, generative modeling of policy weights 1Humanoid loco-manipulation 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 1Letting a humanoid revise its cart-pushing plan mid-motion as it learns the cart's mass and wheel constraints 1Letting humans and coding agents build and tune physics simulations of robot tasks from a phone video 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 1One world model retrained from NVIDIA Cosmos 3 Edge that both predicts scenes and controls 20 robot families 1Panoramic 3D Detection 1Panoramic segmentation 1Pedestrian tracking 1PersonNav 1Planning 1Policy Optimization 1Predicting contact failures before they happen with a vision, action and force world model 1Preference Learning 1Quadruped locomotion 1Real-time VLA 1Real-to-sim 1Real-to-sim-to-real data generation for manipulation 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 1Shrinking a 3.3-billion-parameter robot foundation model into a 451-million-parameter student that runs in two inference steps 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 1Teaching a real humanoid to return tennis balls using public walking data and ordinary tennis videos instead of new motion capture 1Temporal manipulation 1Terrain traversability 1Test-time verification for vision-language-action policies 1Testing whether failure alarms for robot AI models actually save time once false alarms and reset costs are counted 1Text-to-3D Policy 1Tool Co-Design 1Training care robots that help people stand up and transfer, using simulated humans who move back 1Training household mobile manipulators with targeted human corrections instead of more demonstrations 1Trajectory Correction 1Transferring task structure from human videos to robot world-action models 1Traversability 1Turning ordinary human videos into skill-labelled training data for a dexterous robot hand 1Turning phone-recorded human demonstrations into per-robot post-training data by retargeting object motion 1Turning third-person human videos of table-tennis serves, pickups and ladder climbing into Unitree G1 skills by preserving where and how fast contacts happen 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 inference efficiency 1VLA manipulation 1VLA robustness 1VLA robustness benchmark 1Video world models 1Vision-language-action models without wrist cameras (learned foveation) 1Visual Generalization 1Visual Grounding 1Visual Reward Models 1Visuomotor Fusion 1Visuotactile manipulation 1Whether labelling messy human demonstrations by quality improves robot foundation models 1Whole-body mobile manipulation and teleoperation 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
按技术筛选：机器人基础模型，清除
主题页
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。