MEKAWIRE
Research

Papers

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

408 papers
TopicAllManipulation · 65VLA · 43Navigation · 24Learning · 23Robotics · 22Perception · 20Humanoid · 15Safety · 9Simulation · 9Locomotion · 8World Action Models · 4Aerial · 2Aerial 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 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 · 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
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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arXiv
Multi-agent motion planningExpert

Training-Free Diffusion Planning with Analytical Local Scores

Michael Y. Fatemi, Jinhao Liang, Ferdinando Fioretto

This arXiv preprint introduces TFDP, a training-free 2D multi-agent path planner that replaces learned diffusion scores with analytical local scores for smoothness, obstacle avoidance, velocity, and inter-agent separation. On standard benchmarks it reaches 96–100% success while averaging 0.59 s per plan versus 88.23 s for the strongest diffusion baseline, and it scales to 300 agents and 100 obstacles in 5.39 s on an A100 GPU. The work matters because it retains diffusion-style iterative refinement without needing trajectory datasets or map-specific training.

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arXiv
Multi-fidelity RLExpert

Multi-Fidelity Policy Gradients Stabilize Data-Scarce Reinforcement Learning

Xinjie Liu, Ruihan Zhao, Anirban Chaudhuri, Cyrus Neary, Ufuk Topcu, David Fridovich-Keil

This preprint introduces MFPG-PPO, an algorithm that uses abundant low-fidelity simulator data as a control variate to reduce variance in scarce high-fidelity policy-gradient updates. On the hardest simulated locomotion task it matches PPO trained with 16 times more high-fidelity data, and on a physical Franka arm it reaches 91.7% success with only 4 real episodes per update, versus 55.0% for PPO on high-fidelity data alone. If validated, the approach could make on-policy robot learning much more sample-efficient in real-world deployments.

RL
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arXiv
Multimodal simulationExpert

TouchTherm: Building Multimodal Digital Twins of Objects for Tactile and Thermal Rendering

Yitao Zhang, Hong Ying, Haoran Guo, Xiaoying Zhou, Guanyu Chen, Chenxi Xiao

TouchTherm is a preprint framework that turns real objects into simulation-ready digital twins combining a coarse visual mesh, registered tactile micro-height fields, and a physics-regularized dynamic thermal field. On 20 objects it predicts held-out surface temperature with MAE 0.465°C at 30 s and 0.592°C at 45 s, and improves synthetic-to-real tactile object recognition from 20.0% to 34.0% Top-1 accuracy over coarse geometry. The work matters because it closes part of the simulation-to-real gap for contact-rich robotics and VR by making tactile and thermal cues queryable in simulation.

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arXiv
Embodied AgentsExpert

Reconstruct, Practice, Go Real: Guided Self-Improvement for Embodied Agents

Yen-Jen Wang, Haozhe Jiang, Shuying Deng, Haoru Xue, Weirui Ye, Rocky Duan, Nika Haghtalab, S. Shankar Sastry, Pieter Abbeel, Haozhi Qi

This preprint introduces RPG, a framework that uses offline demonstration videos, simulator practice, and privileged-state rollouts to autonomously improve reusable robot skills and prompts without updating model weights. On held-out initializations of 22 manipulation tasks, RPG reaches 95.0% success after 15 practice rounds, beating ASPIRE at 75.5% and CaP-Agent0 with GPT-6 Astra Pro at 60.0%, and succeeds in all 30 physical trials across three tasks. The result matters because it shows shared skill code and a system prompt can be a practical target for autonomous robot capability improvement.

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arXiv
GraspingIntermediate

Continual Learning for 6-DoF Grasp Synthesis via Experience and Demonstrations

Giulio Schiavi, Andrei Cramariuc, Michael Pantic, Roland Siegwart

This preprint presents a continual-learning grasping system that adapts through memory rather than weight updates. After pretraining in simulation, it matches strong 6-DoF baselines offline and improves during deployment from grasp outcomes and optional user demonstrations; in real-robot trials it exceeds 90% success on five of six challenging object categories after at most 50 adaptation attempts. This matters because it gives deployed arms a low-cost way to improve on unfamiliar objects without gradient-based retraining.

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arXiv
Aerial manipulationExpert

AFD-CAMLs: Agile Force-Distribution-Aware Planning and Control for Cable-Suspended Aerial Multi-Lifting Systems

Antreas Kourris, Sihao Sun

This preprint presents AFD-CAMLs, a hybrid planning and control framework for cable-suspended payload transport with four or more UAVs. It combines null-space force-allocation planning, force-aware NMPC, and an admittance filter that uses onboard tension estimates to balance cable loads. In agile real-robot flights, it improved 3D payload tracking error from 0.179 m to 0.153 m and reduced aggregate tension-tracking RMSE from 3.51 N to 2.09 N versus the baseline, without sacrificing agility.

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arXiv
Active vision manipulationExpert

ActiveWAM: Evidence-Aware Active Vision for World-Action Models

Renjun Wu, Luzhou Ge, Xuesong Li

ActiveWAM is a preprint describing a world-action model that jointly controls a dual-arm robot and a two-degree-of-freedom pan/tilt camera, learning when to keep a useful view and when to move to gather new evidence. It reaches 80.3% clean and 53.3% compound-shift success on the 50-task RoboTwin-AV benchmark, versus 76.3% and 33.3% for Fast-WAM, and completes 50.0% of two-stage physical kitchen tasks versus 23.3% for Fast-WAM. This matters because it removes test-time inversion and viewpoint annotations while improving active-vision manipulation under distribution shift.

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arXiv
Underwater perceptionExpert

SonarVoxNet: Diver Detection in 3D Bounding Box using 3D Sonar

Eugene Park, Jiwon Lee, Seyoung Kan, Trung Dong, Xiaomin Lin, Jane Shin

This preprint introduces SonarVoxNet, a 3D sonar detector that predicts full 9-degree-of-freedom oriented bounding boxes for divers, plus Diver3D, a public 37,010-frame dataset with full 3D orientation labels. With augmentation and causal refinement, SonarVoxNet reaches 0.774 [email protected] and 0.719 SDS on the held-out test set, far above LiDAR-derived baselines that top out at 0.374 [email protected]. The work shows that pitch and roll of a freely swimming diver can be recovered from sparse acoustic returns, which matters for AUV diver-robot interaction.

LiDAR
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Topic pages

Manipulation · 65VLA · 42Navigation · 24Robot learning · 23General robotics · 22Perception · 20Humanoid · 15Safety · 9Simulation · 9Locomotion · 7World 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.