MEKAWIRE
Research

Papers

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

9 papers
TopicAllManipulation · 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
LevelAnyBeginnerIntermediateExpert
Filtered by technology dexterous-manipulation · clear
arXiv
ManipulationExpert

AutoBio+: Benchmarking Dexterous Manipulation with Dynamic Experimental Phenomena in Realistic Laboratories

Xuanye Wu, Yue Shi, Yongcan Yang, Yiqun Wang, Zhaoqi Wan, Dayu Xia, Jiezhang Cao, Xiangfei Li, Yao Mu, Guangtao Zhai

Automating biological laboratories requires robots to perform dexterous tool interactions under realistic visual conditions while handling dynamic experimental phenomena, presenting significant challenges for robotic manipulation. To enable systematic evaluation under these conditions, we introduce AutoBio+, a simulation benchmark and framework for dexterous manipulation in realistic biological laboratory environments.

Dexterous Manipulation
Read the full paper (PDF)
arXiv
ManipulationExpert

Human2Dex: Dexterous Robot Learning from Wrist-Centric Human Demonstrations

Jiachen Zhang, Yuanyuan Li, Shaqi Luo

Collecting dexterous manipulation demonstrations for each robot hand requires repeated task-specific data acquisition. We ask whether one shared corpus of natural human demonstrations can instead serve as a reusable data interface for different dexterous hands, without collecting task demonstrations on each target robot.

Dexterous Manipulation
Read the full paper (PDF)
arXiv
VLAExpert

Beyond Actions: Learning Future Operation Targets for Vision-Language-Action Models

Min Lin, Bokai Zhou, Jianqi Lin, Kaiyuan Yang, Yicheng Chang, Junjie Yu, Xiaofan Liu, Jianhua Han, Rui Li, Shen Zhao, Xiaodan Liang

Actions specify what a robot should execute, but do not describe what those commands will achieve. Vision-language-action (VLA) policies nevertheless learn primarily by predicting action chunks from images, language, and state.

VLADexterous Manipulation
Read the full paper (PDF)
arXiv
Dexterous manipulationExpert

PointWAM: 3D World Action Modeling for Dexterous Robotic Manipulation

Chunghyun Park, Beomjun Kim, Seungcheol Park, Heeseung Kwon, Yashu Shukla, Seunghoon Sim, Jinwoo Shin, Minsu Cho

PointWAM is a preprint describing a 3D world action model for dexterous robotic manipulation. It represents the scene and hands as 3D point trajectories in a shared space-time frame, and is pretrained on 1.15 million human demonstration episodes before fine-tuning on robot data. On ten DexJoCo tasks it reaches 69.0% average success, 11.7 points above GR00T N1.6, and on a real humanoid it beats robot-pretrained VLAs by up to 25 points on square-peg insertion.

Dexterous Manipulation
Read the full paper (PDF)
arXiv
Dexterous ManipulationIntermediate

DexJoCo-X: Benchmarking Action Representations for Multi-Hand Dexterous Manipulation

Xiangwei Jiang, Yao Mu, Lixin Duan, Wen Li

This preprint introduces DexJoCo-X, a simulation benchmark and toolkit for comparing action representations across seven dexterous hands and six manipulation tasks using 2,100 balanced demonstrations. Under a single joint policy, function-aligned action slots reach 47.7% mean success over all 42 hand-task pairs, versus 47.0% for native joint coordinates and 33.1% for learned cross-hand latents. The work matters because it isolates representation choices under matched data and training, helping teams build scalable cross-embodiment dexterous policies.

Dexterous Manipulation
Read the full paper (PDF)
arXiv
Dexterous manipulationExpert

FlashDexRetarget: Accelerating Dexterous Manipulation Data Generation through Multi-Motion Retargeting

Kyungmin Lee, Sibeen Kim, Dongyoon Hwang, Yoonsang Oh, Donghu Kim, Youngdo Lee, I Made Aswin Nahrendra, Jaegul Choo, Hojoon Lee

This preprint introduces FlashDexRetarget, a reinforcement-learning framework that jointly retargets many human hand-object demonstrations into dexterous robot trajectories with one policy. On a 50-motion benchmark it reaches 90% retargeting success on the XHand using about 29 GPU-hours, compared with 46% at roughly 3,000 GPU-hours for CHORD. The approach matters because it could make large human demonstration datasets practical for training dexterous robots.

Dexterous Manipulation
Sharpa
Read the full paper (PDF)
arXiv
World-Action ModelsExpert

Direct Experience World-Model Optimization: Learning the World Beyond Action Imitation

Xiangcheng Zhan, Zirui Chen, Yicheng Zhao, Ziteng Gao, Shuo Yang

This arXiv preprint introduces DEWO, a post-deployment learning method for world-action models that learns from observed visual futures around interaction turning points, not just action imitation. Across five simulated dexterous tasks, DEWO improves average success for multiple world-action model variants, and on two real dexterous-hand platforms it lifts success on initially viable spatial cells from 51.0% to 71.7% over two deployment rounds. The work matters because it shows continued predictive learning from deployment experience can make dexterous policies more reliable.

Dexterous Manipulation
Read the full paper (PDF)

Topic pages

Manipulation · 27VLA · 21Perception · 12General robotics · 11Navigation · 10Humanoid · 9Robot learning · 8Safety · 5World Action Models · 5Simulation · 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.