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
Multi-UAV State EstimationExpert

Towards Agile Vision-Based Multi-UAV Flight: Revisiting State Estimation

Michal Pliska, Matouš Vrba, Ondřej Víta, Martin Jiroušek, Viktor Walter, Martin Saska

This preprint introduces vision-based pose-aware state estimators for neighboring multirotor UAVs, integrating visual tilt measurements to infer thrust direction, including a new linear thrust-constrained Kalman filter. Across two real-world and one simulated dataset, pose-aware methods cut mean velocity and acceleration errors by 40% and 57% versus position-only filters, remove a ~300 ms acceleration delay, and enable simulated follower tracking of >2 g lateral maneuvers where position-only estimation fails. This matters for collision avoidance and coordination in agile multi-UAV operations.

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arXiv
VLAIntermediate

ChunkTrust: Adapting Execution Horizons for Robot Policies with Action-Expert Evidence

Fanding Huang, Jingyan Jiang, Shifeng Bao, Mingkang Pu, Shiwei Li, Jing Xu, Shijia Xu, Guanbo Huang, Chenghao Gu, Yuzhi Huang, Chenxin Li, Faisal Nadeem Khan, Huan Yang, Yan Wang, Cheng Chi, Zhi WangTsinghua University, Beijing Academy of Artificial Intelligence (BAAI), Renmin University of China, Shenzhen Technology University, Hefei University of Technology, Jiangnan University, Chongqing University, The Chinese University of Hong Kong

Robot AI models plan a short burst of movements at a time, and how many of those moves the robot carries out before it looks again is usually fixed by hand. This preprint from Tsinghua University, BAAI and partners adds a plug-in that picks that number while the robot works, raising success rates of existing Physical Intelligence and NVIDIA models in simulation and on a real two-arm robot without retraining them.

VLARobot Foundation ModelsImitation Learning
Physical IntelligenceNVIDIA
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arXiv
Autonomous ExplorationExpert

GPU-Accelerated Path-Dependent Marginal Information Gain for Autonomous Exploration

João Félix Mendes, Rodrigo Ventura, Meysam Basiri

This preprint introduces a GPU-accelerated method for path-dependent marginal information gain in autonomous exploration, using per-node depth buffers instead of voxel hash maps. It stays within 5–10% of exact marginal gain while accelerating evaluation up to 118× on a desktop GPU and 28× on a Jetson Orin NX, and in real UAV tests it cut time to 95% coverage by 30%.

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arXiv
Model-based RLExpert

Beyond Policy Alignment: Closing the Planning-Learning Loop for Robot Control with Learned World Models

Kowndinya Boyalakuntla, Yuhan Liu, Abdeslam Boularias

This preprint extends policy-constrained TD-MPC into PL-MPC by changing critic training targets, MPPI terminal values, and actor distillation without altering the world model or planner. On HumanoidBench, it lifts total average return from 98±18 to 387±255 on balance-hard and from 199±13 to 466±200 on hurdle; in zero-shot sim-to-real wrench–nut alignment on a KUKA IIWA14 it achieves 74.2% vs 61.3% success on the training object size. The result matters because it shows targeted interactions in the planning–learning loop can improve hard robot control tasks beyond the usual planner–policy alignment.

World Models
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arXiv
Trajectory CorrectionIntermediate

Markovian Dynamics Enforcer: Feasibility Preserving Correction on Learned Dynamics Manifolds

Kevin Yu, Tao Guo, Constantinos Antoniou, Panagiotis Angeloudis

This is a preprint that introduces MaDE, a frozen post-hoc operator that projects arbitrary neural trajectory predictions onto a learned dynamics manifold while inferring unobserved controls and reducing inequality violations. On inD intersection data, it cuts the one-step kinematic-bicycle residual from about 0.17 for raw predictors to 0.007, and cuts combined inequality violations from 3.6–5.8% to 0.7–1.1%, at the cost of raising average displacement error by 1.57–1.83x. This matters because it gives robot and vehicle pipelines a plug-and-play way to make data-driven forecasts dynamically and constraint consistent without retraining predictors.

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arXiv
Terrain traversabilityExpert

Experience-Driven Continual Learning of Terrain Traversability for Quadruped Robots

Luca Bricarello, João Carlos Virgolino Soares, Alberto Sanchez-Delgado, Fulvio Mastrogiovanni, Claudio Semini

This preprint presents a quadruped traversability system that predicts five foot-contact outcomes from pre-contact camera images using a frozen DINOv3 backbone and an evidential regressor, then continually updates the model through replay with a historical validation gate. On a sequential real-robot stream across three unseen surface types, the gate cut anchor negative log-likelihood degradation by 23.1% versus replay without the gate while keeping new-terrain adaptation nearly equal. It matters for legged robots that must operate safely on unfamiliar ground without forgetting earlier experience.

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arXiv
Real-time VLAExpert

Toward Real-Time VLAs: Stage-Aware Two-Step Flow Denoising and System-Level Evaluation

Di Wu, Rongtian Shen, Ping Liu, Yan Shen, Zhenhan Yin, Shun Zuo, Xuhua Chen, He Zheng, Lingfeng Zhang, Jianglin Zhang, Tao Zhang

This preprint reports a two-stage non-uniform Flow Matching denoiser that cuts the π0.5 VLA's action-generation steps from 10 to 2 and model-inference time from 61.6 ms to 22.0 ms, a 2.8× speedup. The authors also built a distributed real-time VLA execution framework and benchmarked six action-scheduling methods on a 180-trial bimanual physical garment-folding task; Legato achieved 96.7% success as the best training-based method, while Temporal Smoothing led training-free methods at 76.7%. Combining the fast sampler with those methods gave 1.68–2.80× inference speedups at a 6.7–10.0 percentage-point success loss, highlighting a practical speed–quality tradeoff for real-time robot policies.

VLA
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arXiv
Text-to-3D PolicyIntermediate

Text-to-3D Policy: Fine-Grained Language-Behavior Alignment for Unseen Specification Generalization

Xinhao Yang, Wenhao Wu, Ning Lv, Yanshen Ding, Zhenhong Sun, Daoyi Dong, Chunlin Chen, Zhi Wang

This preprint introduces T3DP, a text-to-3D policy framework that aligns instruction tokens with local behavior segments from demonstrations to improve generalization to unseen fine-grained behavior specifications. Across Meta-World, ManiSkill, and RoboTwin, T3DP improves average held-out-specification success over global language-behavior alignment by +11.0 to +14.2 points; on real-robot tasks it raises average success from 47.5% to 65.0% (+17.5 points). The result matters for robot systems that must follow precise language such as target positions, displacements, or articulated states without exhaustive demonstration coverage.

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arXiv
Safe RL-MPCExpert

RL-Guided PAC-NMPC for Probabilistically-Safe Perception-Based Navigation in Unknown Environments

Adam Polevoy, Dillon Capalongo, Katherine Tang, Mark Gonzales, Marin Kobilarov, Joseph Moore

This preprint introduces AC-PAC-NMPC, a hybrid controller that places RL-trained actor, critic, and sensor-prediction models inside a sampling-based stochastic NMPC framework to provide finite-time probabilistic safety bounds and long-horizon vision-based navigation. On a fixed-wing UAV with an onboard depth camera, it raised real-robot success from 40% (both RL actor and best PAC-NMPC baseline) to 80% with the lowest cost; in simulation it reached 90% success versus 85% for the strongest baseline. This matters for agile robots that need learned long-range behavior without sacrificing formal collision-probability guarantees.

RL
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arXiv
Embodied policyExpert

EWAM: Emergent Depth-Wise Specialization in a Unified Embodied Model -- From Semantic Understanding through Visual Foresight to Action

Hao Wang, Jiajun Wen, Jingzhi Liu, Shuoshuo Xue, Zhiliang Chen, Min Lin, Yicheng Chang, Xiaoyu Guo, Yukang Zhuo, Zheng Chong, Yunshuang Nie, Jian Zhang, Weijia Liufu, Qingman Wu, Heming Xu, Bingchang Song, Dantong Wu, Zhiyuan Wang, Hang Xu, Jianhua Han

This preprint introduces EWAM, a unified embodied model whose action tokens asymmetrically attend to vision-language, video, and action experts. It reports 72.1% clean-to-random success on RoboTwin 2.0, a 92.9% in-domain average, 98.8% on LIBERO, and top real-robot results across three embodiments. The work matters because it shows a learned depth-wise handoff from semantic understanding to visual foresight to action can improve robustness and transfer, including from low-cost human egocentric data.

VLA
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arXiv
Social NavigationIntermediate

Social-WM: Safety-Aware Latent World Models for Robot Social Navigation

Zhihao Zheng, Mooi Choo Chuah

Social-WM is a preprint describing a latent world-model planner for robot social navigation that learns future latent states and action realizability from egocentric RGB video. On Social-HM3D it reaches 63.77% success while reducing human collisions to 21.67%, and transfers zero-shot to Social-MP3D. This matters because it improves pedestrian safety without explicit pedestrian tracking, privileged human state, or online reinforcement learning.

World Models
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arXiv
ManipulationIntermediate

RoboCoach: World Models as Active Coaches for Compositional Robot Skills

Jiajun Liu, Yifan Chen, Yichao Liu, Jiayi Zhang, Ruoqu Chen, Shaoxuan Xie, Guocai Yao, Mengdi Xu, Sen Cui, Changshui Zhang

This preprint introduces RoboCoach, a world-model-guided coaching loop that decides which subtask demonstrations to collect and which reusable skill expert adapters to update. On real robots, 150 added subtask demonstrations lift complete-task success from 13.3% to 75.0% on Franka and from 40.0% to 83.8% on AgileX, and coached skills transfer to four unseen compositions where a baseline scores 0%. The work matters because it shows world models can direct scarce real-world supervision toward the specific reusable skills that fail, rather than requiring expensive end-to-end data.

World Models
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arXiv
NavigationExpert

Comparing Utility of Inertial, Occupancy, Semantic, and Intent Information in Human Motion Prediction During Daily Tasks

Max Burns, Maisha Khanum, Monroe Kennedy, Steven H. Collins

Accurate human motion prediction is crucial for robotic systems operating around people, particularly in complex indoor spaces. In this study, we assess the relative importance of different sources of information in indoor motion prediction with a human motion diffusion model.

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arXiv
ManipulationExpert

MVG-WAM: Multiple View Geometry-Aware World-Action Modeling for Robotic Manipulation

Wenbo Chen, Tianfu Li, Haoxuan Xu, Zhihao Cao, Zhenghan Chen, Zhengming Zhu, Zizhou Luo, Guosheng Yang, Yuan Liu, Lujia Wang, Wen Chen, Haoang Li

World-Action Models (WAMs) couple visual dynamics with action prediction, bringing the rich priors of pretrained video models to robotic manipulation. However, their multi-view interfaces typically tile images or concatenate tokens, leaving the geometric relationships among synchronized cameras implicit.

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arXiv
VLAExpert

MotorMind: Scaffolding General Vision Language Models for Zero-Shot Robot Manipulation

Bingxuan Li, Siqi Song, Yizhuo Wu, Jiarui Yao, Tong Zhang, Huan Zhang

Vision-language-action (VLA) models have advanced robotic manipulation, but their zero-shot generalization in new tasks and environments remains limited, and their reliance on specialized training keeps them from benefiting directly from rapidly advancing general-purpose vision-language models (VLMs). In parallel, recent agentic robotic systems leverage VLMs for high-level reasoning or coding agents for robot control, but often depend on extensive external models and tools, introducing additional complexity and cost.

VLA
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arXiv
ManipulationExpert

BlenDAgger: Blended Shared Control for Interactive Imitation Learning

Cailyn Smith, Geoffrey Sun, Henny Admoni, Zackory Erickson

Robot policies are frequently trained from human corrections, yet teleoperating a robot to provide corrections is burdensome, and human demonstrators are not always optimal. We propose Blended DAgger (BlenDAgger), an approach for collecting data to train imitation learning policies by using shared control to blend the policy's and demonstrator's actions during interventions.

Imitation Learning
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arXiv
HumanoidExpert

EgoAlign: Bridging the Human-Humanoid Gap for Long-Range Loco-Manipulation

Yiming Jiang, Chen Jin, Chongyang Xu, Yilun Chen, Aimin Hao, Yisheng He

Egocentric human demonstrations offer an accessible source of task experience, but differences in body scale and controller response, together with missing robot states, limit their value as humanoid training supervision. We present EgoAlign, a data-construction framework that converts these demonstrations into action and state supervision compatible with a general-purpose, continuous whole-body controller, without collecting physical-robot demonstrations.

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arXiv
LearningExpert

Rethinking Representations for World-Action Modeling

Haoyi Jiang, Liu Liu, Xinjiang Wang, Zhihao Sun, Zequn Chen, Sen Wang, Xinjie Wang, Xia Chen, Jingfeng Yao, Weiheng Zhao, Shanglin Yuan, Zhizhong Su, Wei Sui, Wenyu Liu, Xinggang Wang

World-action models jointly learn robot policies and predict future observations, making the representation space an interface between control and prediction. We study the design of this space through controlled comparisons, finding that neither reconstruction fidelity nor pre-trained perceptual features alone ensure effective policy learning.

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arXiv
NavigationExpert

Geometry-Preserving Human-to-Robot Upper-Body Motion Retargeting from Monocular Video

Xiaoyu Yang, Sen Han, Da Li, Nan Wu

Monocular RGB video provides an accessible source of human demonstrations for upper-body robot motion, yet video-driven human-to-robot transfer remains challenging because body and hand motion are recovered at different spatial scales, human and robot kinematics differ substantially, and fine distal motion is difficult to preserve across embodiments. We present a geometry-preserving motion-retargeting framework that integrates unified body--hand reconstruction with morphology-independent geometric transfer.

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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.