MEKAWIRE
Research

Papers

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

199 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
World Action ModelsExpert

XGenAct: Geometry-Enhanced World Action Models through Cross-Task Generation

Tingting Du, Ziyao Wang, Guoheng Sun, Ang Li

XGenAct is a preprint describing a world action model that turns RGB observations, robot actions, metric depth, surface normals, and functional-role segmentation into RGB video streams and trains one video diffusion transformer on all of them. On five held-out RLBench tasks it averages 52% closed-loop success versus 26% for the strongest evaluated baseline. This matters because it adds explicit geometric and semantic future prediction to robot action generation without modality-specific heads or auxiliary losses.

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arXiv
VLAExpert

FastOPD: On-Policy Distillation for Lightweight VLA Deployment

Yoojin Oh, Jeongsol Kim, Yeonwoo Seo, Jangho Park, Seonghyun Jin, Sunwoo Park, Youngmin Kim, Youngjun Jun, Kyumin Choi, Jong Chul Ye

This preprint introduces FastOPD, a method for distilling large vision-language-action (VLA) and world-action-model teachers into compact few-step robot policies. It keeps 84% of a 3.3B-parameter teacher's LIBERO success rate with only two sampling steps, reduces inference latency by 78.1%, and on RoboTwin 2.0 lifts single-step SmolVLA success from 35.3% to 51.2%. The work matters because it may allow foundation-policy capabilities to run on resource-constrained, low-latency robot hardware.

VLA
Foundation
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arXiv
Safety monitoringExpert

Localized Conformal Safety Monitoring with Vision-Language Models for Autonomous Driving

Luís Marques, Rong Fang, Disha Kamale, Dmitry Berenson

This preprint introduces SLLCP, a post-hoc calibration layer over frozen vision-language models that turns raw safe/unsafe guesses into probabilistic safety prediction sets for autonomous driving. On 15,321 unseen CARLA frames, SLLCP flags 89.6% of collision-causing trajectories with Qwen3-VL-8B and 88.4% with Cosmos-Reason2-8B, versus 4.6% and 39.1% for the raw binary VLMs. It matters because it offers a planner-agnostic way to reduce missed unsafe trajectories before execution, albeit with more false alarms.

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arXiv
Representation alignmentExpert

On Representational Alignment among Embodied Agents

Fulvio Mastrogiovanni

This preprint formalizes a task-dependent stopping condition for aligning representations across embodied agents. In an analytical hand-off scenario, the same ambiguity needs zero added sensorimotor anchors for relative synchronization but exactly one for absolute timing, because only the interaction invariance changes. This matters for multi-robot and human-robot systems: it suggests targeting alignment effort only where unresolved differences can change the task outcome.

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arXiv
Monocular SSCExpert

CrowdOcc: Monocular Semantic Scene Completion for Quadruped Robots in Crowded Indoor Environments

Feiyang Chen, Jincheng Hu, Yiduo Chen, Jihao Li, Yue Liang, Bingzhao Gao, Yanjun Huang, Yuanjian Zhang

The authors present CrowdOcc, a preprint introducing an RGB-D dataset of 25.1K quadruped-captured frames in crowded indoor scenes and a monocular semantic scene completion model with surface-normal fusion and sparse human-centric 3D interaction. On held-out scenes it reaches 15.80 IoU, 11.40 mIoU, and 46.23 Human IoU, outperforming prior baselines and improving human, wall, and floor reconstruction. This matters because onboard single-camera dense 3D occupancy for low-viewpoint robots in cluttered human environments remains underexplored.

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arXiv
Byzantine localizationExpert

Who Went Where When on the Lunar Surface: Forensic Trajectory Analysis to Identify Byzantine Rovers

Lachlan Holden, Feras Dayoub, Melissa de Zwart, David Harvey, Tat-Jun Chin

This preprint introduces a forensic trajectory analysis method for co-located lunar rover missions where some rovers may provide miscalibrated or deliberately falsified telemetry. An attribution-aware credibility method rejects whole non-credible rovers rather than individual outlier measurements and reconstructs trajectories from the remaining sources. In simulation it recalled up to 90.8% of non-credible measurements versus under 8% for robust pose-graph baselines, and on planetary-analogue data it was the only method to unambiguously expose a safety-zone incursion.

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arXiv
PersonNavExpert

CSIR: Contextually and Socially Informed Robots for Efficient Person Goal Navigation

Tyler Chung, Nahl Farhan, Hao Zhang, Mingfeng Yuan, Jinjun Shan, Amy Wu, Yue Hu

This preprint introduces CSIR, a planning framework for indoor person-goal navigation that combines semantic likelihoods from uncertain natural language requests with distance-aware route optimization. In synthetic benchmarks across 1,500 scenarios in three environments, CSIR achieved an average travel efficiency of 0.62, above the best classical baseline at 0.43 and comparable LLM-TRP at 0.60. It matters because it reduces dependence on private activity logs and fixed schedules, making service robots more deployable under realistic privacy and information-uncertainty constraints.

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arXiv
Runtime VerificationExpert

Mind the Refinement Gap: When Safe High-Level Robot Plans Produce Unsafe Executions

Stabak Das, Priyesh Ranjan, Xiangfang Li, Lijun Qian

This preprint audits a trace-completeness assumption in RoboGuard by comparing safety verdicts on surface plans and graph-refined traces under the same LTL specification. In 12 controlled targeted cases the surface plan was accepted while the refined trace was rejected, and in end-to-end SPINE-RoboGuard tests 2 false-safe cases occurred. The work motivates lightweight graph-based trace refinement as a fix for formal safety monitoring of language-enabled planners.

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arXiv
Miniature LocomotionExpert

Around the World: Unified Learned Locomotion on a 270 g Continuous-Rotation Quadruped

Arturo Flores Alvarez, Nathan Lintu, Dennis Hong

This preprint presents a single learned locomotion policy for MiNI-Q, a 270 g quadruped with continuous-rotation legs, running entirely onboard at 50 Hz. It reports forward-velocity RMSE of 0.037 m/s upright and 0.050 m/s inverted, 24/30 release recoveries, and successful walking with four foot geometries across four surfaces without retraining. The result shows that continuous-rotation topology can unify upright/inverted locomotion, posture changes, and recovery in one compact controller for sub-kilogram robots.

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arXiv
Panoramic segmentationExpert

EmbPASS: Towards Cross-Embodiment Open Panoramic Segmentation

Pujun Guo, Yuanfan Zheng, Fei Teng, Mengfei Duan, Guoqiang Zhao, Yuheng Zhang, Kai Luo, Kailun Yang

The authors introduce EmbPASS, a 1,000-image panoramic semantic segmentation benchmark spanning vehicle, drone, wearable and quadruped platforms, and EPONet, a CLIP-based open-vocabulary segmentation network for cross-embodiment panoramic perception. On EmbPASS, EPONet reaches 35.82% mIoU, 1.10 points above the strongest baseline, and also leads DensePASS. This is a preprint.

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arXiv
Egocentric videoExpert

World Action Learning via Interaction-Centric Spectral Latent Guidance

Zhiming Liu, Yikun Miao, Ying Chen, Hongrui Yin, Fangqi Zhu, Xiaoyi Pang, Quanxin Shou, Zhengyang Yan, Haodong Wang, Song Guo

This preprint introduces WING, a framework that learns interaction-centric latent actions from egocentric human videos and transfers them to robot policies through low-frequency spectral guidance. In simulation it reaches 99.20% average success on LIBERO, 93.80% on RoboTwin 2.0, and 57.7% on RoboCasa–GR1, and on real-world bimanual tasks it achieves 75.0% average success in the standard setting. It matters because it offers a scalable path to use abundant egocentric video instead of costly robot teleoperation for robot learning.

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arXiv
Visual GeneralizationExpert

VIGOR: Zero-Shot Visual Generalization via Latent-Space Consistency in Model-Based Reinforcement Learning

Mingyu Park, Samyeul Noh, Hyun Myung, Donghwan Lee

This preprint introduces VIGOR, a model-based reinforcement learning framework that adds latent-space consistency to TD-MPC2 for zero-shot generalization to unseen visual distractions. On six DeepMind Control Suite tasks and four Robosuite tasks, it improves mean zero-shot returns by 3.4% over the second-best baseline on DMC and by 43.6% on Robosuite while matching TD-MPC2's sample efficiency. The work matters because it addresses a two-level compounding failure unique to recursive latent dynamics, which is critical for deploying sample-efficient MBRL in visually varying environments.

RL
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arXiv
Mobile manipulationExpert

MobiAgent: Dual-Loop Recursive Policy Self-Improvement for Long-Horizon Mobile Manipulation

Chenzhi Liu, Yue Zhang, Jiehong Lin, Jianan Wang, Bo Wang, Zhongrui Wang, Xiaojuan Qi

This preprint introduces MobiAgent, a dual-loop framework for long-horizon mobile manipulation. It decouples high-level VLM planning from low-level flow-matching skills and adds an automated lifelong learning loop from deployment data. The abstract reports no quantitative results, but the approach matters because it targets compounding errors and interference between locomotion and arm control.

VLA
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arXiv
Humanoid ControlExpert

KungfuAthleteBot: learning high-dynamic humanoid motion from video with unified robust recovery

Zhongxiang Lei, Lulu Cao, Xuyang Wang, Tianyi Qian, Jinyan Liu, Xuesong Li

KungfuAthleteBot is a preprint pipeline that turns monocular video of elite martial artists into high-dynamic motions for a Unitree G1 humanoid by repairing trajectory physics, using physics-driven low-kinetic-energy initialization, and training one unified policy for tracking plus fall recovery. On real hardware the robot tracks a five-minute Tai Chi sequence and recovers from arbitrary falls in about 0.7 s, reported as the fastest for a unified policy. This matters because it shows video can replace expensive motion capture for extreme humanoid skills when reconstruction, missing actuation, and recovery are addressed together.

Humanoid
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arXiv
Multi-view perceptionExpert

Less Decoder is More Encoder: Geometric Representation Learning from Novel View Synthesis

Keerthi Kaashyap, Dennis Anthony, Akshay Krishnan, Nhi Ngoc Nguyen, Jeremy Collins, James Hays, Shreyas Kousik, Animesh Garg

This preprint introduces SNAP, a self-supervised encoder-decoder transformer that learns transferable geometric scene representations from novel-view synthesis without dense 3D labels. The headline result is that SNAP matches or beats self-supervised baselines on five geometry tasks and approaches heavily geometry-supervised models such as VGGT, while staying robust to camera shifts in robot manipulation. This matters because it shows posed RGB video may replace expensive 3D annotation for spatial foundation models.

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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
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arXiv
Simulation geometryExpert

I2CD: Direct Image-to-Convex Decomposition for Simulation-Ready Collision Geometry

Qian Wang, Liam Merz Hoffmeister, Brian Scassellati, Daniel Rakita

This preprint presents I2CD, a method that turns a single RGB image into simulation-ready convex collision geometry by freezing a pretrained Hunyuan3D-2 image-to-3D model and training a small 38M-parameter head. On 227 held-out objects it achieves the best volumetric IoU (65.7%) among eight reconstruction-plus-decomposition baselines and runs 6–37x faster end-to-end (about 0.5 seconds per image). In physical xArm7 pick-and-place, it reduces the time to planner-ready scene geometry from 328 seconds to 11 seconds with 85/100 vs 90/100 success.

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arXiv
JEPA World ModelsExpert

AVL-JEPA: Preventing Causal Dynamics Information Collapse In Joint Embedding Predictive Architecture World Models

Yikang Qiao, Ling Zhang, Ziying Song, Duan Huang

This arXiv preprint introduces AVL-JEPA, a training method that stops joint-embedding world models from discarding action-conditioned physical dynamics while preserving visual representations. Across four simulated robot control tasks, it reaches 81.5% success on PushT and 88.7% on TwoRoom under Gaussian input noise, versus 15.3% and 65.7% for the baseline, while maintaining competitive clean-environment performance. The work matters for robot planners that must stay reliable when camera or state observations are noisy.

World Models
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arXiv
ExoskeletonsExpert

Subject-Specific Predictive Musculoskeletal Simulations of Lower-Limb Exoskeleton Assistance: Metabolic and Biomechanical Effects of Joint Assistance Strategies

Neethan Ratnakumar, Mariya Tohfafarosha, Xianlian Zhou

Preprint. Researchers built subject-specific musculoskeletal walking simulations with idealized hip, knee, and ankle actuators and tested all single- and multi-joint combinations at 25 and 50 Nm. At 50 Nm, three-joint hip–knee–ankle assistance cut predicted metabolic cost by 48.50% on average, while hip–ankle assistance captured most of that benefit at 44.60%, supporting simpler two-actuator designs. The results provide simulation-derived guidance for exoskeleton developers but still need hardware validation.

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arXiv
Visuotactile manipulationExpert

Equivariant Visual-Tactile Diffusion Policy for Contact-Rich Manipulation

Lik Hang Kenny Wong, Yiyao Ma, Xiu-Shen Wei, Zelong Tan, Zhuheng Song, Dongsheng Xie, Kai Chen, Qi Dou

This preprint introduces VISTA, a diffusion-policy architecture that fuses wrist camera and tactile images into a rotation-equivariant spherical representation for contact-rich manipulation. In simulation with 50 demonstrations per task it reaches 80.9% mean success, outperforming an equivariant tactile baseline by 15.9 points, and on three real-robot tasks it succeeds in 7.33/10 trials versus 1.33/10 for that baseline. The design could reduce the demonstration data needed for tight-insertion and surface-contact tasks by encoding workspace rotation symmetry into visuotactile perception.

Imitation Learning
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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.