MEKAWIRE
Research

Papers

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

72 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
Lab automationIntermediate

A Passive AI System for Verifying Physical State on Automated Liquid Handlers

Junqiong Joanne Qiu, Zeckria Kamrany, Ananya Anand, Emma Vidal, Yunjia Johanna Bai, Nathan S Chen, Sean Son, Varada Abhyankar, Taryn Jakub, Eleazar Eskin

Preprint. The authors built a passive, external vision system called Labware Setup Checker that verifies deck preparation on an Opentrons Flex liquid handler without modifying the robot's hardware or firmware. It correctly identified a completed plate-and-base assembly in all 29 pre-motion frames and matched the protocol reference in 95.1% of 348 exact-match labware classifications. This matters because it adds non-intrusive setup verification that could reduce manual deck errors in lab automation.

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arXiv
Bimanual ManipulationIntermediate

EyeRobot 2.0: Active Gaze for Precise Manipulation without Wrist Cameras

Kush Hari, Justin Kerr, Nidhya Shivakumar, Samarth Mahapatra, Carmelo Sferrazza, Jiahui Lei, Jitendra Malik, C. Karen Liu, Ken Goldberg, Angjoo Kanazawa

This preprint presents EyeRobot 2.0, a bimanual manipulation framework that uses a single fixed stereo camera with active gaze: two eye viewpoints swivel to a 3D fixation point, and the system feeds foveated multi-resolution crops to a behavior-cloning gripper policy. In 7 real and 6 simulated tasks, it lifts real static-stereo success from 27% to about 66.5% (roughly +40 percentage points), matches wrist-camera policies with a 69% vs 64% success rate when wrists are unobstructed, and beats them 48% vs 22% when grasped objects occlude the wrists. This matters because removing wrist cameras can simplify end-effectors and keep vision usable during tool use, clutter, and occlusions.

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arXiv
Multi-Agent RLIntermediate

Permutation Robustness Is Not Enough: Action Collapse in Multi-Agent Transformer Policies

Amit Thakur, Mukesh Singhal

This preprint examines transformer policies for cooperative multi-robot navigation and shows that measuring permutation equivariance alone is misleading: low error can occur because all agents output the same action, not because the policy is robust. In a PettingZoo/MPE cooperative navigation task, a PPO baseline with learned agent ID/position embeddings is diverse but order-sensitive, while equivariance regularization reduces order error but can collapse actions, especially when moving from 3 to 4 agents. The work argues for reporting task return, permutation consistency, and action diversity together.

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arXiv
Contact-Rich ManipulationIntermediate

Learning Reflexive Behavior for Contact-Rich Manipulation

Quan Nguyen, Yunho Kim, Joonho Lee

This preprint introduces a learned proprioceptive reflex policy that sits between task-level commands and joint control. Trained in simulation on spring, plane, and rail interaction primitives, it keeps contact forces near thresholds when commands or geometry are imperfect; on real peg insertion with 0.02 mm clearance, it raises success from at most 22% to 36–58% and uses less than half the mean estimated contact force of IK+PD. The result matters for assembly, humanoid lifting, and surface following as a reusable low-level execution layer under planners, learned policies, and teleoperation.

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arXiv
Sim-to-Real ManipulationIntermediate

Skill2Real: Agentic Skill Learning for Zero-Shot Sim-to-Real Robot Manipulation

Xincheng He, Siyu Ma, Chang Yu, Yunuo Chen, Yanjia Huang, Ying Nian Wu, Yin Yang, Chenfanfu Jiang

Skill2Real is a preprint describing an agentic framework that learns reusable robot manipulation skills in simulation and transfers them zero-shot to real hardware through a shared code-based policy interface. On a physical UR5e, it raises mean task completion from 27.50% with no learned skills to 78.75% across four tasks, and on unseen long-horizon LIBERO-Pro Long tasks it raises GPT-6 Astra success from 2.0% to 56.3% using frozen skills. This matters because it shifts sim-to-real learning from adapting low-level representations to acquiring validated, composable task knowledge without real-robot fine-tuning.

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arXiv
ManipulationIntermediate

Proprioceptive Sketches as Long-Horizon Intent for Generative Action Policies

Fangyuan Wang, Songhao Huang, Haoxiang Sun, Shipeng Lyu, Chengyang He, Anqing Duan, Peng Zhou, David Navarro-Alarcon

The paper presents Proprioceptive Action Models (PAM), a preprint that adds a compact, timing-free B-spline sketch of the robot’s remaining joint-space path to short-horizon action generation inside one diffusion transformer. Across real-world dual-arm tasks, PAM-DP raises success from 47.5% to 75.0% and also improves Push-T and LIBERO-Long. This offers long-horizon intent without costly language plans or video forecasting.

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arXiv
World Action ModelsIntermediate

Completion Aware Guidance for World Action Models

Seungyeon Kim, Junhoo Lee, Baekseung Kim, Minkyu Kim, Nojun Kwak

This preprint introduces Completion Aware Guidance (CAG), a training-free sampling method that steers world action models to generate task-completing video-action chunks instead of plausible but incomplete continuations. On a RoboTwin 2.0 subset it lifts average success from 64.4% to 70.0%, and on DreamZero zero-shot simulation from 69% to 75%, while cutting task-incomplete imagination from 79% to 40%. It matters because it improves existing WAMs without retraining, reducing a distinct failure mode in short-horizon robot control.

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arXiv
Streaming 3D reconstructionIntermediate

CLoSeR: Closing the Loop for Long-Context Streaming Reconstruction

Moyang Li, Zihan Zhu, Wei Zhang, Marc Pollefeys, Daniel Barath

This preprint presents CLoSeR, a loop-closure system built on the streaming feedforward reconstructor LoGeR for long monocular videos. It detects revisits with global image descriptors, re-runs the backbone on loop-conditioned windows, and optimizes all poses on SE(3), cutting average trajectory error substantially on kilometer-scale benchmarks (e.g., 11.15 m vs 36.16 m for LoGeR on VBR). Because the backbone already has consistent global scale, this avoids the fragile point-cloud alignment and higher-dimensional pose graph optimization used by earlier submap methods.

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arXiv
Humanoid safetyIntermediate

Continue, Abort, or Fall: Viability-Aware Policy Selection (VAPS) for Safe Humanoid Acrobatics

Siwei Ju, Lu Liu, Jan Peters, Oleg Arenz

Preprint. VAPS is a safety layer for humanoid acrobatic robots that adds two backup policies—an abort policy that lands on its feet and a protective-fall policy—and selects among them plus the nominal tracking policy using short-horizon viability predictors. In simulation on a LimX Oli, it cut head-contact rate from 15.5% to 1.1% while keeping motion success nearly unchanged (79.2% vs 80.7%), and on the physical robot the full hierarchy achieved its intended objective in 15 of 18 side-flip trials. The work matters because it directly addresses the main cost of deploying dynamic humanoids: hardware damage from failed maneuvers.

Humanoid
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arXiv
Pedestrian trackingIntermediate

Beyond Leaderboard Scores: A Deployment-Focused Protocol for Interpretable Tracking Evaluation in Pedestrian-Centric Environments

Dominik Wojcikiewicz, Diego Paez-Granados

This preprint presents a deployment-focused evaluation protocol for 3D pedestrian multi-object tracking that holds detections fixed and reports trajectory-level success rates for initialization, detector-gap continuation, identity recovery, close-neighbor association, and load-dependent runtime on a Jetson Orin. On JRDB, non-GT-assisted trackers score only 24.26–29.67% HOTA; after a 1.0 s detection gap, no non-GT tracker maintains correct same-ID spatial output in more than 45.4% of cases, and most fall below 1%. The protocol shows aggregate leaderboard scores hide large behavioral differences that matter for real-time robots among pedestrians.

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arXiv
Robot-Use AgentsIntermediate

OpenRUA: Robot-Use Agents Are Zero-Shot Visuomotor Policies

Zhaoyang Chu, Earl T. Barr, Claire Le Goues, Peter O'Hearn, Mark Harman, Federica Sarro, He Ye

OpenRUA is a preprint describing a zero-abstraction harness that lets off-the-shelf coding agents control a robot through its native ROS 2 terminal interface, without bespoke primitives or learned policies. In simulation, Claude Code powered by Claude Opus 5 reached 99.0% success on CaP-Bench and 87.0% on LIBERO-PRO, and on RoboCasa365 it held unseen composite task performance at 28.1% while baselines collapsed. This matters because it suggests frontier coding agents plus a mature robot middleware stack may be a practical zero-shot alternative to training VLAs or hand-engineering agent wrappers.

ROS 2
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arXiv
Navigation & MappingIntermediate

GlassGuard: Verified Glass Plane Mapping for Robot Navigation

Hanwen Guo, Zhengzhi Lin, Yusen Xie, Ji Zhang

GlassGuard is a preprint system that reconstructs planar architectural glass for LiDAR navigation by combining a pruned SAM3 vision model, LiDAR-derived structural pillars, depth-free orientation checks, and persistent map management. On nine real-world scenes and 2.1 km of traversal, its panoramic version covers 85.2% of glass with 15.3 false voxels per frame, while the pinhole version covers 82.1% versus at most 61.0% for baselines and carries 5–17× fewer false voxels per frame. It matters because it blocks collision paths through glass without contaminating traversable space, a dual requirement for safe robots in glass-walled buildings.

LiDAR
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arXiv
Federated 3D PerceptionIntermediate

FedCKA: Representation-Guided Layer Personalization for Federated 3D Perception Across Driving Domains

Jolle Verhoog, Ali Burak Ünal, Holger Caesar

This preprint presents FedCKA, a federated learning method that uses layer-wise Centered Kernel Alignment scores to decide which parts of a multimodal 3D object detector should stay local versus be shared across clients. On a five-domain nuScenes split covering Boston and Singapore, day/night and clear/rain, FedCKA reaches a validation-weighted NDS of 0.635, versus 0.545–0.559 for the strongest prior personalized baselines, about 7 percentage points higher. It matters because it removes the need for hand-picked layer partitions or fixed personalization ratios in cross-fleet driving perception.

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arXiv
Humanoid Tool UseIntermediate

HumanoidToolBench: Benchmarking Humanoid Tool Use from Selection to Mobile Execution

Kyochul Jang, Seohyeon Park, Ohchul Kwon, Sangjun Park, Junhyeok Choi, Seungyeop Yi, Chaeyun Kim, Sangkyu Lee, Idan Szpektor, Avi Caciularu, Jongmin Park, Youngjae Yu

HumanoidToolBench is a preprint benchmark and dataset for humanoid tool use, containing 18 tasks across three scenarios, three execution levels, and standard/decoy tool sets, along with 3,094 teleoperated demonstrations on simulated and real Unitree G1 robots. Seven policies were evaluated in simulation and three on the real robot, revealing large gaps between selecting the right tool and actually completing the task. Focused GR00T N1.7 probes further show reduced selection accuracy on unseen tools and continued execution under unrelated instructions.

Humanoid
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arXiv
Quadruped locomotionIntermediate

PROMO: Preference-conditioned Multi-Objective Reinforcement Learning for Quadrupedal Robots

Amr Mousa, Rifny Rachman, Neil Karavis, Michele Caprio, Richard Allmendinger

In this preprint, PROMO conditions a single quadruped locomotion policy on a three-way semantic preference over tracking, stability, and energy efficiency. On a Unitree Go2, changing only the preference reduces specific energy by up to 30.4%, position error by 38.7%, and peak body-attitude deviation by 59.0% relative to a balanced setting. This gives legged robots an operator-facing trade-off dial without retraining or switching controllers.

RL
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arXiv
Failure recoveryIntermediate

Recova: Agent-Guided Failure Recovery for Autonomous Robotic Manipulation

Isabella Liu, An-Chieh Cheng, Johan Bjorck, Zhiding Yu, Hongxu Yin, Jan Kautz, Linxi Fan, Yuke Zhu, Sifei Liu

This preprint presents Recova, an agent-guided framework that jointly develops task execution and failure recovery in a reconstructed digital twin and refines both through real-robot data collection. It attains 78.8% mean success on LIBERO-Pro and 64.9% on MolmoSpaces, versus 71.7% and 38.0% for the strongest baselines; on four real-robot tasks it reaches 87.5% success and cuts observed human intervention from 87.5% to 0% over four collection rounds. The work matters because it turns failures into reusable recovery skills and reduces the human supervision needed to keep manipulation policies running.

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arXiv
Motion PlanningIntermediate

RUL-Aware RRT*: Degradation-Balanced Motion Planning for Robotic Manipulators

Haibo Li, Zhiguo Zeng, Xu Li

This preprint introduces RUL-aware RRT*, a motion planner that adds joint remaining-useful-life penalties to standard RRT* so a robotic manipulator distributes wear across actuators. In simulated UR5 pick-and-place trials it extended time-to-first-failure by 48–56% in a localized-degradation scenario, and improved bottleneck lifetime by 23.8–29.2% in a heterogeneous-degradation scenario. It matters because it connects predictive maintenance to path planning rather than leaving health information at the monitoring layer.

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arXiv
Surgical roboticsIntermediate

Ex vivo breach detection using electrical conductivity during robotic pedicle drilling in the spine

Jorge Andrés Pérez Velásquez, François Teyssere, Thibault Chandanson, Quentin Grimal, Brahim Tamadazte

This preprint presents a robotic pedicle-drilling system that uses a SpineGuard electrical bioimpedance drill tip and a real-time dual-threshold algorithm to stop drilling before a spinal canal breach. In ex vivo tests on 51 fresh porcine vertebrae, it prevented breaches in 100% of cases, with 6 grade A and 45 grade B results and a mean canal deformation of 0.7 mm. The work offers an imaging-free safety layer for robotic spine surgery.

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arXiv
Underwater RoboticsIntermediate

Towards Physical Underwater Robotic Assistance for Scuba Diver Movement in Confined Spaces

Demetrious T. Kutzke, Junaed Sattar

Preprint: The authors built RADMCS, a low-cost wearable thruster pack that straps to a scuba tank and gives divers haptic yaw cues to hold a set distance from walls or structures. In pool and ocean tests with eight divers, perceptible guidance required only about 10% of maximum thruster force; with stable visual distance estimates, the robot issued correct left/right corrections. This suggests a practical alternative to bulky exoskeletons for diver safety in confined underwater work.

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arXiv
Multi-Robot ManipulationIntermediate

DuoMind: Enabling Distributed Multi-Robot Coordination with Semantic Communication

Hanchu Zhou, Dechen Gao, Hang Wang, Brendan Lynch, Boqi Zhao, Qiyao Ma, Raman Goyal, Junshan Zhang

In this preprint, the authors introduce DuoMind, a distributed multi-robot framework that pairs per-robot VLM orchestrators with low-level VLA action policies and structured semantic messages. Across two simulation benchmarks, it raises long-horizon success substantially over finetuned single policies, for example 39.25% vs 1.00% on Cook Pot and 78.00% vs 52.25% on Hang Bag. The work matters because it shows multi-robot coordination can be added to off-the-shelf foundation models without centralized control or joint multi-agent training.

VLA
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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.