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ロボティクスとフィジカルAIの最新論文と、それぞれが産業にもたらす意味。

論文1本
テーマすべてマニピュレーション 91VLA 62ナビゲーション 52ロボティクス全般 41ロボット学習 32シミュレーション 24知覚 23ヒューマノイド 19歩行移動 15安全性 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 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 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 1Grounding 1Humanoid Control 1Humanoid RL 1Humanoid Tool Use 1Humanoid control, generative modeling of policy weights 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 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 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 1Test-time verification for vision-language-action policies 1Text-to-3D Policy 1Tool Co-Design 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 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 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
難易度指定なし入門中級上級
技術「Sim-to-Real転移」で絞り込み中、解除
arXiv
Whether labelling messy human demonstrations by quality improves robot foundation models上級

品質を考慮したVLA事前学習で何が効くか：ベンチマークと実証研究

Mingxuan Yan, Litian Gong, Dihong Huang, Zhixu Li, Tianyu Zhang, Wenqian Zhang, Hefeifei Jiang, Peihao Li, Jian Zhang, Mengfei Zhao, Yikai Tang, Hai Zhai, Zehao Wang, Ruijian Liang, Yanjia Huang, Lin Shao, Changliu Liu, Kaiyu Hang, Zhiwen Fan, Masayoshi TomizukaGeorgia Institute of Technology, Axis Robotics, UC Riverside, CMU, UC Berkeley and others

人の遠隔操作には迷いや遠回りがつきものだ。こうしたデータに品質ラベルを付ければロボットモデルが良くなるのかを検証したプレプリントで、効果はあるが小さくモデル次第だった。むしろ単純なシミュレーション事前学習で実機の成功率が9.6%から31.7%に上がった。

模倣学習VLASim-to-Real転移ロボット基盤モデル
Physical IntelligenceHugging FaceFranka Robotics
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テーマ別ページ

マニピュレーション 8VLA 4World Action Models 5World-action model 3Aerial manipulation 3Dexterous manipulation 3Sim-to-Real Manipulation 3Social Navigation 3

論文は arXiv のロボティクス分野から取得しています。当社の要約が未作成の場合は、要旨の冒頭を表示します。