MEKAWIRE
Research

Papers

New robotics and physical-AI papers, with what each one means for the industry.

1 papers
TopicAllManipulation · 91VLA · 62Navigation · 52General robotics · 41Robot learning · 32Simulation · 24Perception · 23Humanoid · 19Locomotion · 15Safety · 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
LevelAnyBeginnerIntermediateExpert
arXiv
Whether labelling messy human demonstrations by quality improves robot foundation modelsExpert

What Matters in Quality-Aware VLA Pretraining: A Benchmark and Empirical Study

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

Human teleoperators hesitate and take detours, and robot-model builders want to know whether tagging that sloppy data by quality helps. This preprint finds the gains are real but small and model-dependent, while plain simulation pretraining lifted real-robot success from 9.6% to 31.7%.

Imitation LearningVLASim-to-Real TransferRobot Foundation Models
Physical IntelligenceHugging FaceFranka Robotics
Read the full paper (PDF)

Topic pages

Manipulation · 91VLA · 62Navigation · 52General robotics · 41Robot learning · 32Simulation · 24Perception · 23Humanoid · 19Locomotion · 15Safety · 13Aerial · 4World Action Models · 5World-action model · 3Aerial manipulation · 3Dexterous manipulation · 3Sim-to-Real Manipulation · 3Social Navigation · 3

Papers come from arXiv robotics feeds. Where our summary is not written yet, you see the opening lines of the abstract.