MEKAWIRE
Research

Papers

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

5 papers
TopicAllManipulation · 41VLA · 28Navigation · 16Perception · 16Learning · 13Robotics · 13Humanoid · 12Safety · 7Simulation · 7Locomotion · 5World Action Models · 4Aerial 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 · 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
LevelAnyBeginnerIntermediateExpert
Filtered by technology world-model · clear
arXiv
World modelsIntermediate

DeltaWorld: Physically Consistent Interactive World Simulators via Action-Conditioned Latent Increment Learning

Boyuan Hou, Xiaoge Cao, Chaofan Zhang, Shuo Wang, Shaowei Cui

DeltaWorld is a preprint describing an interactive world simulator for robot manipulation that predicts action-conditioned latent increments instead of full future latent states. On a self-collected cross-robot dataset it reduces FVD by 46.6% and LPIPS by 31.1% relative to the IWS baseline, and on the IWS benchmark it improves FID from 44.16 to 36.04. This matters because more stable long-horizon action-conditioned video prediction could reduce real-robot rollouts for policy training and evaluation.

World Models
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arXiv
Video world modelsIntermediate

PhysicsLENS: Diagnosing Physical Property Blindness in Video Generation Models

Isaiah Milkey, Som Sagar, Aditya Taparia, Xinyuan Liu, Jiqing Wen, Ransalu Senanayake

This arXiv preprint introduces PhysicsLENS, a benchmark that tests whether robot video generation models follow text-specified physical properties that are not visible in the starting frame. Across four video generators and 439 clips, human raters found 34 of 47 plausible-looking clips ignored the stated latent property, a gap current visual-quality evaluations miss.

World Models
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arXiv
Social NavigationIntermediate

Social-WM: Safety-Aware Latent World Models for Robot Social Navigation

Zhihao Zheng, Mooi Choo Chuah

Social-WM is a preprint describing a latent world-model planner for robot social navigation that learns future latent states and action realizability from egocentric RGB video. On Social-HM3D it reaches 63.77% success while reducing human collisions to 21.67%, and transfers zero-shot to Social-MP3D. This matters because it improves pedestrian safety without explicit pedestrian tracking, privileged human state, or online reinforcement learning.

World Models
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arXiv
ManipulationIntermediate

RoboCoach: World Models as Active Coaches for Compositional Robot Skills

Jiajun Liu, Yifan Chen, Yichao Liu, Jiayi Zhang, Ruoqu Chen, Shaoxuan Xie, Guocai Yao, Mengdi Xu, Sen Cui, Changshui Zhang

This preprint introduces RoboCoach, a world-model-guided coaching loop that decides which subtask demonstrations to collect and which reusable skill expert adapters to update. On real robots, 150 added subtask demonstrations lift complete-task success from 13.3% to 75.0% on Franka and from 40.0% to 83.8% on AgileX, and coached skills transfer to four unseen compositions where a baseline scores 0%. The work matters because it shows world models can direct scarce real-world supervision toward the specific reusable skills that fail, rather than requiring expensive end-to-end data.

World Models
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Topic pages

Manipulation · 41VLA · 27Navigation · 16Perception · 16Robot learning · 13General robotics · 13Humanoid · 12Safety · 7Simulation · 7Locomotion · 4World Action Models · 5Aerial 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.