MEKAWIRE
Research

Papers

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

276 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
arXiv
Agricultural RoboticsIntermediate

ALFRED: Requirement-driven development of an open-source mobile manipulator for long-term plant monitoring

Ciarán Miceal Johnson, Christopher Quail, Garry Ellard, Alistair McConnell, Steve Tonneau, Fernando Auat Cheein

Preprint. The authors built ALFRED, an open-source mobile manipulator made from commercial parts, and ran it for a year of monthly forest surveys (528 traversals) with no missed collection. Model-based comparisons across three builds show usable arm workspace rising from 34.0% to 66.1% of reachable poses and only the final build keeps the frame-mounted LiDAR clear forward and backward. This matters because it provides a reproducible, requirement-driven platform and release package for repeated close-range plant monitoring.

LiDAR
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arXiv
VLA manipulationExpert

ChunkVLA-AM: Parallel Action Chunking for Vision-Language-Action Robot Control in Additive Manufacturing

Zhugang Liu, Kaichuang Zhang, Jinman Zhang, Pu Sun, Martha Asare, Jose Hernandez, Maxim Ermolinsky, Efren Saenz, Qi Lu, Jinghao Yang

This arXiv preprint describes ChunkVLA-AM, a deployment pipeline that adapts an OpenVLA-OFT vision-language-action model to a FAIRINO FR3 robot for additive-manufacturing post-print retrieval. The robot predicts and executes 8-step chunks of 7-D Cartesian actions in a cloud-edge loop and completed 39 of 42 physical pick-and-transfer trials, a 92.9% success rate. The work provides a concrete feasibility check for using VLA-based automation in a fixed manufacturing workcell.

VLA
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arXiv
Safety-critical controlExpert

Finite-Data Safety Informativity Under Dynamic Asymmetric Actuation

Abhinav Sinha, Praveen Kumar Ranjan, Yongcan Cao

This preprint presents a finite-data safety certificate that checks whether a commanded input can keep a partially unknown nonlinear system within prescribed output bounds even when actuators have asymmetric limits and bounded tracking error. In a simulated vehicle-tracking study, a data-selection strategy produced certificates at 76.27% of 11,241 validation queries, versus 32.09% for uniformly sampled and 1.03% for consecutively sampled records, and an exact-parameter benchmark reached 86.26%. The work matters because it gives a deterministic, model-uncertainty-aware safety filter for high-assurance control where a reliable dynamics model is unavailable.

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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
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arXiv
VLA ConditioningIntermediate

Continuous Conditioning of VLAs with Augmenting EMG and Visual Task Descriptors

Edward W. Staley, Connor O. Pyles, Rahul Hingorani, Frank Camargo, Griffin Milsap, Jared Markowitz, Matthew S. Fifer, Michael Wolmetz

This preprint tunes action-expert vision-language-action models to accept non-language task cues: 8-channel EMG envelopes or visual segmentation annotations. On a real cube-picking task, EMG conditioning lifts cluttered success from 30% to 50% and cuts failure rate from 50% to 4%; on 16 simulated RoboCasa pick-and-place tasks, visual annotations lift cluttered success from 11% to 20% and cut failure from 47% to 25%. The findings suggest downstream VLA policies can usefully condition on continuous cues beyond fixed language prompts, especially in clutter.

VLA
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arXiv
3DGS datasetExpert

3DROID: A Renderable 3D Gaussian Dataset with Measured Per-Scene Reliability

Wonguen Cho, Junhoo Lee, Nojun Kwak

This preprint introduces 3DROID, a DROID-derived dataset that turns 114 robot manipulation episodes into renderable 3D Gaussian scenes anchored to the robot's metric workspace, with per-scene calibration and reconstruction-reliability metadata. Combining refined camera extrinsics with pose conditioning improves held-out view PSNR from 12.57 to 15.81 dB for YoNoSplat and from 12.75 to 16.27 dB for ZipSplat. The work matters because view-consistent synthetic robot data are useful for manipulation only if they preserve real-world scale and robot-frame alignment.

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arXiv
Marine simulationExpert

H-SPAR: Hydrodynamic-aware Simulation for Particle Transport and Autonomous Robots

Navid Zarrabi, Nariman Yousefi, Sajad Saeedi

This arXiv preprint describes H-SPAR, an open-source ROS 2/Gazebo simulator that couples a shared precomputed flow field to both current-induced drag on an uncrewed surface vehicle and Lagrangian advection of sampled particles. In path-planning tests, SVF-RRT* cuts planned upstream cost by 69.4% versus RRT*, but the advantage falls to 41.7% during execution with time-varying currents; sweep orientation shifts particle sampling rates by up to 22.2% under the full hydrodynamic configuration. It matters because marine autonomy and particle-sampling performance can now be benchmarked together under consistent hydrodynamics.

ROS 2
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arXiv
Unified world-action modelExpert

UniWAM: Unified World-Action Model

Jiayi Chen, Wenxuan Song, Jingbo Wang, Shuai Zhou, Xicheng Gong, Zehua Fan, Ziyang Zhou, Junwu E, Haodong Yan, Fuhao Li, Qize Yu, Xu Huang, Pengwei Wang, Wen Chen, Shunbo Zhou, Haoang Li

UniWAM is a preprint introducing a unified robot foundation model that combines a vision-language reasoner, a video-generation world model, and an action predictor. It reaches 99.2% success on LIBERO, 92.6% on LIBERO-Plus OOD perturbations, 68.32% on RoboTwin 2.0 Clean2Random, and 82.5% real-robot instruction-following rate. The work matters because it shows how co-training on robot, human egocentric, and VQA data can improve robustness, generalization, and long-horizon manipulation.

VLA
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arXiv
Articulation EstimationIntermediate

Query-Conditioned Articulation Estimation from a Single Image

Abdelrhman Werby, Fabio Scaparro, Kai O. Arra

QueryArt is a preprint model that estimates revolute or prismatic articulation parameters from a single calibrated RGB image, a 2D query point, and camera intrinsics, then uses one depth reading at the query to recover metric scale. It outperformed recent baselines on four held-out benchmarks and two out-of-distribution datasets, and achieved 70.2% success across 57 real-robot manipulation trials on unseen furniture. This matters because it decouples where to interact from how the part moves, making single-view explicit kinematics practical for mobile manipulators.

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arXiv
ManipulationExpert

Keep the Effect, Drop the Actor: Programmable Effect-to-Execution World-Action Models

Junyi Hu, Zhewen He, Zhenhua Li, Yi Fang

PEWAM is a preprint describing a 71.5M-parameter world-action model that compiles robot demonstrations into editable “effect programs”—3D object keypoint trajectories, grasp contact points, and final scene configuration—rather than actor-specific motions. On held-out LIBERO-Goal tasks, one program from a single demonstration completes 40 of 90 episodes, while the same backbone under goal-image or language conditioning and prior demonstration-conditioned methods complete at most 19. This matters because turning demonstrations into coordinate-based programs enables editing and cross-robot-arm reuse, a potential route to more flexible robot programming.

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arXiv
VLA RobustnessIntermediate

Is Success All You Need? Investigating the Impact of Input Perturbations on VLA Behaviour in Tabletop Manipulation Tasks

Sophie Higham, Riccardo Andrea Izzo, Matteo Matteucci, Alessandro Suglia

This arXiv preprint introduces a benchmark-agnostic evaluation framework that measures how successful VLA-controlled trajectories change under input perturbations, going beyond task success rate. Testing three state-of-the-art VLA models across four LIBERO suites and seven perturbation conditions, it finds that perturbed trajectories can still succeed yet diverge substantially in motion smoothness, efficiency, and gripper behaviour. This matters because teams relying only on success rate may miss behavioural degradation that affects safety and reliability.

VLA
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arXiv
Multi-Robot ExplorationIntermediate

OpenSpace Lab Solution to the IROS 2026 Indoor Exploration Competition

Yuxuan Zhang, Dong Li, Zezhou Sun, Yuxuan Xu, Siyu Teng, Yuchen Li, Jianjian Yang, Long Chen

This preprint describes the OpenSpace Lab entry for the IROS 2026 Indoor Exploration Competition. It combines neural map-completion predictions, budget-aware goal selection, and homing constraints for single-robot exploration, with a coordinated multi-robot extension using shared maps and intent penalties. The system placed first in the single-robot public track at 61.04% coverage and third in both private tracks (39.53% single, 39.91% multi).

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arXiv
Geometry-aware manipulationExpert

Robot Learning on Discrete Surfaces: Theory and Applications

Matteo Dalle Vedove, Fares J. Abu-Dakka, Luigi Palopoli, Daniele Fontanelli, Matteo Saveriano

Preprint. The authors build a discrete Riemannian framework that lets robots learn motion and functions directly on polyhedral surface meshes rather than treating surfaces as external constraints. They apply it to Dynamic Movement Primitives, Gaussian Processes, and Riemannian Flow Matching, showing stability improvements over MeshDMP and roughly halving RFM training time. This matters for any contact-rich surface task such as polishing, drawing, or inspection on CAD or reconstructed meshes.

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arXiv
Compositional ManipulationExpert

Rethinking World-Action Model for Compositional and In-Context Robotic Manipulation

Shukai Gong, Xuanran Zhai, Yintianrun Zhang, Ruopeng Cui, Ye Huang, Yiyang Fu, Dexuan Lyu, Chaojie Li, Xinyi Song, Peiwen Lin, Chuang Wang, Mingyuan Jia, Yufan Deng, Jiaxin Fang, Bo Liang, Jiaxin Li, Yuxiang Gao, Hao Liu, Daquan Zhou

This preprint introduces ViGAR, a hierarchical manipulation model that separates visual subgoal planning from subgoal-conditioned world-action generation, using a shared pretrained Cosmos3-Nano world model. On the RoboTwin Clean2Random benchmark it reaches 82.00% Clean and 67.02% Random success, 12.86 points above the best baseline average. It matters because image-based subtask goals allow the same policy to be steered to new task compositions via a global goal image without retraining.

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arXiv
Endoscopic navigationIntermediate

ColoACT: Multi-Cue Action Chunking for Smooth Autonomous Colon Navigation on a Self-Propelled Endoscopic Robot

Jian Hu, Shujing He, Leixin Chang, Zongze Li, Ding Huang, Chaoyang Shi, Chengzhi Hu

This is a preprint describing ColoACT, an imitation-learning navigation system for a compact self-propelled endoscopic robot. The policy fuses RGB, relative depth, and a gradient-based pseudo-elevation map with an Action Chunking Transformer and temporal ensembling. In ex-vivo porcine colons it reached 85.4% success on straight and 72.5% on curved segments, with smooth steering and success on 90-degree turns (70%) and double bends (60%), suggesting a path toward reducing physician burden in colonoscopy.

RL
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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
Policy OptimizationExpert

FERPO: Forward Entropy-Regularized Policy Optimization

Sebastian Sanokowski, Alireza Sarmadi, Majid Khadiv

This arXiv preprint introduces FERPO, an on-policy maximum-entropy reinforcement-learning algorithm that updates a policy from critic values without taking critic action derivatives. It roughly matches REPPO on DMC and ManiSkill while reaching high scores with fewer environment interactions, and at K=16 its uncached actor-critic updates are 6.9% faster than REPPO, or 22.2% faster with cached actor updates. The method matters for massively parallel robot-policy training because it avoids unreliable learned action gradients and improves wall-clock and sample efficiency.

RL
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arXiv
PlanningIntermediate

Cross-entropy optimization with prioritized constraints

Francisco Roldan Sanchez, Pau de las Heras Molins, David Fridovich-Keil, Georgios Bakirtzis

This preprint introduces TierCEM, a variant of the cross-entropy method that enforces an ordered sequence of constraints during elite selection, removing the need for per-constraint penalty weights. In exact and learned world-model Push-T tests, reversing the priority order switched which constraint was violated, and putting task progress first let lower-priority constraints relax so progress continued. For robot planners facing conflicting safety and task requirements, this offers a more explicit and tunable-free way to specify which rules yield first.

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arXiv
Humanoid loco-manipulationExpert

InterEvolve: Test-Time Evolution of Reward Programs for Humanoid Loco-Manipulation

Zhuo Lin, Sirui Xu, Liuyu Bian, Yu-Xiong Wang, Liang-Yan Gui

InterEvolve is a preprint framework that evolves staged reward programs at test time so a frozen humanoid controller can solve new loco-manipulation tasks without retraining. It raises average success from 34.6% for the best calibrated fixed reward to 86.5% across eight goal-conditioned task families and transfers evolved skills to a real Unitree G1 using onboard perception. This turns task adaptation into a search over objectives rather than policy retraining, with more test-time compute improving results.

Humanoid
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arXiv
World-action modelIntermediate

SkeleWAM: Skeleton World-Action Modeling for Efficient Robotic Manipulation

Juyi Sheng, Hua Wang, Mengyuan Liu

This preprint introduces SkeleWAM, a 57.1M-parameter world-action model for robotic manipulation that represents scenes as sparse 3D skeletons of robot joints, object centers, and interaction points. On the full LIBERO-Plus benchmark it reaches 85.9% zero-shot success, beating the 2B-parameter Cosmos-Policy by 3.7 points, and 89% average success across five real-robot tasks versus Cosmos-Policy's 87%. The result suggests explicit robot–object geometry can provide robust, parameter-efficient control without video or dense latent world prediction.

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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.