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
Social NavigationIntermediate

Rethinking Legibility in Social Robot Hallway Navigation: Impact of Intent Representation and Human Distraction

Pranav Goyal, Andrew Stratton, Christoforos Mavrogiannis

This preprint evaluates how a robot should convey navigation intent in constrained hallways. In two user studies with the Hello Robot Stretch 2, adaptive passing-side legibility led to smoother human motion and higher competence ratings than destination-based or non-legible baselines, and legible passing-side motion still reduced human acceleration when pedestrians were distracted. The work shows that communicating near-term passing side, not final destination, is key for close-quarters human-robot coordination.

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arXiv
Underwater active mappingIntermediate

Active Mapping of Underwater Litter Using Camera-Sonar Fusion

David Rete, Patrick Boros, Lucian Busoniu

This preprint presents an active underwater mapping system that fuses forward-looking sonar and camera detections in a shared Bayesian occupancy map and chooses the next-best viewpoint by balancing exploration and object exploitation. In a realistic simulator, active dual-sensor search found all 11 litter objects after 27 m of travel, versus 110 m for a lawnmower pattern, and was roughly twice as fast as either sensor alone. The result matters for making litter surveys and underwater inspection more efficient.

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arXiv
Vision-Language NavigationIntermediate

NavHarness: Adaptive Goals for Agentic Vision-Language Navigation

Haoxiang Shi, Zaijing Li, Muhe Ding, Xiang Deng, Yaowei Wang, Liqiang Nie

This preprint introduces NavHarness, an agentic vision-language navigation framework that pairs adaptive local goals with explicit verification and progress-aware multimodal memory compression. With GPT-6-Astra, it achieves 79.0% success on R2R-CE and 82.5% on RxR-CE, improving over Codex CLI by 5.0 and 6.2 points, and reaches 83.3% success with 1.51 m error in real-world quadruped tests. The approach matters because it keeps long-horizon navigation aligned with instructions while reducing the context needed for later decisions.

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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
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
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
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
ManipulationIntermediate

Encore: Few-Shot Agentic Discovery of Manipulation Strategies

Yifan Kang, Zihan Wang, Zhiwen Fan, Bangya Liu

Encore is a preprint framework that distills a small number of teleoperated demonstrations into structured evidence for a coding agent, which writes, runs, and repairs manipulation policy programs without access to a runtime success signal. On 60 perturbed LIBERO-PRO cells, programs built from three demonstrations score 96.3% sealed success versus 94.9% without demonstrations, and on a real bimanual robot two tasks score 9/10 each. The result matters because demonstrations act as a search prior for auditable, frozen code policies rather than opaque imitation networks.

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arXiv
Mining PerceptionIntermediate

Towards Spatial Perception for Heterogeneous Robot Collaboration in Subterranean Mining Environments

Mario Alberto Valdes Saucedo, Akash Patel, Christoforos Kanellakis, George Nikolakopoulos

In this preprint, the authors build an onboard perception pipeline that lets a lightweight Explorer robot find minerals in underground mines from free-text prompts and hand compact, oriented 3D inspection targets to a heavier Inspector robot. Field tests in a test mine and an active magnesite mine detected two mineral types without retraining and condensed about 38,000 raw mineral points into a few dozen polygons, roughly two orders of magnitude smaller. This matters because autonomous mining inspection requires no mine-specific labels and low-bandwidth robot-to-robot handover.

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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.