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
Belief-space planningExpert

BLT*: Informed Belief Localization Trees for Uncertainty-Aware Planning on Digital Twins

Elliot Preston-Krebs, Abhishek Goudar, Timothy D. Barfoot

Preprint. The authors introduce Informed BLT*, a sampling-based belief-space planner that extends RRT*/Informed RRT* using the 2-Wasserstein metric for isotropic Gaussian beliefs. Across 16 simulated and digital-twin maps, BLT* found first solutions faster than B-RRT, B-SST and RRBT in nearly all maps—for example, 0.09 s versus 2.38–5.71 s on a 205×124 m campus digital twin—while solving a larger office twin where baselines mostly failed. This matters for ground robots that must plan paths that stay well-localized, not just collision-free.

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arXiv
5G SimulationExpert

Network-in-the-Loop at Scale: GPU-Batched 5G Simulation for Massively Parallel Robot Learning

Zifan Zhang, Mingzhe Han, Kannan Athreya, Yuchen Liu

Isaac-Net is a preprint describing a GPU-batched 5G New Radio network simulator that runs in lockstep with Isaac Lab physics, enabling thousands of parallel robot-learning environments to share realistic uplink contention. It reproduces ns-3 5G-LENA median delay within roughly 5 to 10 percent, low on an unseen carrier and high at 32 robots per environment in closed loop, and captures Age of Information better than an independent per-message delay. This matters for training multi-robot policies that need to learn network-aware coordination on private 5G fleets.

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arXiv
Multi-UAV ExplorationIntermediate

Centralized Multi-UAV Exploration and 3D Reconstruction Using Single-UAV Planners

João Félix Mendes, Meysam Basiri, Rodrigo Ventura

This is a preprint. The authors built a centralized multi-UAV exploration framework that lets existing single-UAV sampling-based planners work in a team by sharing a common TSDF map, filtering other robots from depth data, and checking inter-robot collisions. In simulation with three UAVs, separated start positions consistently beat joint starts; for example, KAEP reached 95% coverage of the school environment in 5.35 min vs 5.86 min from a joint start. The result matters because system-level integration, not new planner logic, can speed up exploration and improve robustness.

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arXiv
Multi-camera odometryIntermediate

StreamRig: Exploiting Intra-Rig Geometry for Streaming Multi-Camera Odometry

Yufei Wei, Shuhao Ye, Qi Wang, Xin Zheng, Qing Huang, Rong Xiong, Yue Wang

This preprint presents StreamRig, a freeze-and-stream system that adapts a frozen multi-view 3D foundation model into causal streaming odometry for calibrated multi-camera rigs. Across NCLT, TartanGround, KITTI-360, and a self-collected humanoid-robot dataset, it achieves the lowest translation and rotation drift among non-oracle methods, and on an RTX 4090 it processes a five-camera rig in 26.2 ms with 2.6 GiB, faster and lighter than monocular CUT3R. The result matters because it shows how lightweight trainable modules can exploit known rig geometry and temporal state without retraining the foundation front-end, including sim-to-real transfer.

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arXiv
Visual GroundingExpert

GroundAnything: Reconciling Parallel Decoding with Precise Visual Grounding at Flash Speed

Qize Yu, Lianrui Fan, Bowen Ping, Xini Ding, Zetian Song, Junbo Niu, Kaixuan Wang, Tianxing Chen, Yue Chen, Minghua He, Yuran Wang, Jie Huang, Haojun Zhang, Min Chen, Hao Li, Wenxuan Song, Ruihai Wu, Xianming Liu, Shilong Liu, Shuchang Zhou

Preprint. The authors introduce GroundAnything, a 4B-parameter visual grounding model that uses bidirectional diffusion with blockwise denoising for parallel spatial decoding instead of sequential autoregressive token generation. Its autoregressive variant, GroundAnything-VLM, reports 72.42% across 30 grounding benchmarks, ahead of GPT-6 Astra at 71.35%. This matters for latency-sensitive robotics and interactive vision systems that need fast, precise localization.

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arXiv
Co-speech HumanoidIntermediate

ECHO-G: Embodied Co-speech Humanoid mOtion Generation

Yizhao Li, Pusen Gao, Ming Wang, Shaojie Shen, Shuo Yang, Hao Xu

ECHO-G is a preprint framework that generates full-body speech-synchronized motion for humanoid robots from audio and timed transcripts. It models motion directly in robot space with a Speech-Grounded Diffusion Transformer and reports a Fréchet Gesture Distance of 2.278 versus 4.976 for EMAGE+GMR, with 5.96 ms/frame inference time. This matters for making expressive, deployable co-speech gestures on humanoids without human-motion retargeting.

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arXiv
Tool Co-DesignExpert

Tool-Policy Co-Design for Powder Weighing in Laboratory Automation

Nikola Radulov, Xin Yang, Kevin S. Luck, Gabriella Pizzuto

This preprint presents a co-design method that jointly optimizes a powder-dispensing spoon's geometry and its reinforcement-learning control policy for robotic laboratory weighing. In real-robot trials, the best co-designed tool reduced overall weighing error to 2.23±3.00 mg from 4.09±6.82 mg for a standard tool across seven powders, about a 45% relative reduction. The approach addresses a bottleneck in self-driving labs by adapting tools, not just policies, to material flow behavior.

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arXiv
LiDAR SecurityExpert

TACTIC: Temporal and Context-Aware LLM Tactical Planning for Roadside LiDAR Attacks

Yiming Gao, Shaocheng Luo

This preprint presents TACTIC, a scene-aware framework that uses a multimodal large language model and roadside perception to choose and tune physical LiDAR attacks in dynamic traffic. In 280 randomized CARLA trials, the full system caused collisions in 100% of evaluation cases, versus 35% for a fixed rule and 60% for random attack selection. The result shows attack success depends on scene context, which matters for evaluating LiDAR safety failures in autonomous driving.

LiDAR
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arXiv
ManipulationExpert

DynaHarness: A Dynamic Physical Harness for Self-Evolving Robot Agents

Haoyuan Deng, Jiebin Liu, Tengxiao Zhang, Langning Yan, Hongye Cao, Ziwei Wang

DynaHarness is an arXiv preprint that adds a dynamic physical harness around a frozen vision-language-action policy: a slow Qwen3-VL-4B planner proposes capabilities, while a fast 2 Hz governor grounds, monitors, substitutes and recovers them. On LIBERO-Pro it reaches 75.2% success on 800 newly sampled states versus 17.5% for the frozen π0.5 policy, and its self-evolution gains transfer to new states. This gives robot-agent builders a retraining-free way to govern and improve existing policies from execution evidence.

VLA
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arXiv
Manipulation SafetyIntermediate

Multi-Link Safety Filtering for VLA Policies Around Moving Hazards

Yatharth Agarwal, Vijay Raghunathan

This preprint introduces a training-free runtime safety filter that wraps a pretrained vision-language-action policy, shielding the gripper, wrist, and forearm with five ellipsoids and tracking moving hazards via sparse optical flow. Across six simulated hazard-motion conditions it cuts the collision rate from 65.62% to 27.27% and lifts safe task completion from 29.35% to 50.43%, while a small 16-episode physical arm test shows 3/16 shielded contacts versus 11/16 unshielded. The barrier runs in about 2 ms on CPU, showing onboard shielding is feasible without retraining.

VLA
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arXiv
Sonar SLAMExpert

BatSLAM 2.0: Sequence-Verified Sonar Place Recognition in a Robust Pose Graph

Jan Steckel

BatSLAM 2.0 is a preprint describing a sonar-only simultaneous localization and mapping system that uses a biomimetic binaural acoustic front-end, sequence-verified place recognition, and a factor-graph pose graph. In 12 simulated long-range drives with calibrated odometry, it produced consistent maps with no wrong loop closures and a post-alignment trajectory error of 0.18–0.53 m; on a real beamformed sonar recording, it cut error from 3.62 m to 0.67 m. This matters for robots that must operate in darkness, smoke, or dust where cameras and lidar fail.

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arXiv
Social navigationIntermediate

STARS: From Spatiotemporal Dynamics to Social Representations in Human-Robot Interaction

Nathan Tsoi, Michael J. Munje, Tejas Oberoi, Rishab Maheshwari, Pengen Zheng, Tanush Chauhan, Peter Stone, Joydeep Biswas

STARS is a preprint method that learns compact, reusable social representations from short multi-agent navigation windows by treating agents as nodes in a spatiotemporal graph and training a self-supervised graph variational autoencoder. In downstream tests, its heterogeneous variant reaches 0.503 SNS macro F1 for pedestrian action classification versus about 0.30–0.33 for unstructured baselines, while remaining competitive on SEAN-T human perception tasks. This matters because it shows structured graph representations can offset scarce human-robot-interaction data, enabling data-efficient social navigation.

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arXiv
Safe VLAExpert

Learning from Runtime Feedback through Failure-Bank Self-Evolution for Vision-Language-Action Models

Mingyue Cui, Zheyuan Liu, Yihan Zhu, Zheyuan Zhang, Meng Jiang

This preprint presents FailBank, a framework that turns an observe-only safety teacher's runtime corrections into training records for vision-language-action policies. On VLA-Arena static-obstacle tasks, it raises task success by 8.5 and 6.9 percentage points over base policies for two VLA backbones while cutting policy-induced cumulative cost by 35.6% and 23.8%. The method matters because it lets robot policies learn a persistent balance between completing a task and avoiding unintended contact, instead of relying on temporary runtime shields.

VLA
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arXiv
Skill DiscoveryExpert

Game-Guided Skill Discovery through Self-Play for Playable Agent Control

Seungeun Rho, Jeonghwan Kim, Xue Bin Peng, Sehoon Ha

This preprint introduces Game-Guided Skill Discovery (GGSD), a self-play framework that learns a small set of discrete motor skills that humans can directly use to control simulated agents. Across Ant, Franka arm, and Unitree G1 embodiments, GGSD produced semantically distinct skills and let users with brief practice solve unseen maze and cube-push tasks with 84–100% success. It matters because it points to compact, human-playable skill interfaces for high-degree-of-freedom robots without task-specific retraining.

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arXiv
3D AssemblyIntermediate

AssemblyWorld: Rethinking 3D Assembly with General-Purpose Agents

Jiahao Zhang, Yeying Fan, Moitreya Chatterjee, Suhas Lohit, Bernhard Egger, Tim K. Marks, Anoop Cherian, Stephen Gould

This preprint introduces AssemblyWorld, an interactive 3D simulation where general-purpose multimodal agents assemble rigid parts through rendered views and pose-editing tools. The strongest evaluated agent, GPT-6 Astra, reaches 80.9% part accuracy but only 59.4% complete-assembly success across 100 tasks spanning furniture, industrial parts, and broken-object reassembly. It matters because off-the-shelf agents can reason about assembly without task-specific fine-tuning, but precision and execution reliability still fall short of deployment needs.

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arXiv
UAV traffic monitoringIntermediate

Prediction is Better than Detection: Traffic Congestion Control using Drones

Samira Hayat, Christian Raffelsberger

This preprint introduces a Mesa-based multi-agent simulation where UAVs patrol road junctions to detect and predict traffic jams and trigger adaptive signal control. Across sweeps of fleet size, traffic level, and network size, detection and prediction rates plateau once drone count roughly matches junction count; prediction-triggered signal changes cut jam duration by up to 8 ticks versus up to 5 ticks for detection-triggered changes, roughly doubling benefit. It suggests onboard prediction rather than larger fleets is the higher-value path for aerial congestion management.

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arXiv
Memory-centric VLAIntermediate

Inline Memory Meets Reusable Skills: Memory-centric Framework for Vision-Language-Action Model

Zaijing Li, Rui Shao, Bing Hu, Haoyu Zhang, Dongmei Jiang, Liqiang Nie

This preprint introduces Optimus-R, a memory-centric VLA framework that turns robotic manipulation adaptation into explicit query-skill memory tuning instead of repeated parameter updates. With only 30% of training data it raises LIBERO average success to 88.6% (vs 72.1% for π0.5) and reaches 33.3% real-world success from 20 demos per task; in lifelong learning it reduces forgetting to 10.0% vs 15.0%. The approach matters for low-data robot adaptation and retaining prior skills.

VLA
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arXiv
Temporal manipulationIntermediate

PhasePlan: Ordered Future-Phase Planning for Robot Brain Models

Xiaoyu Yang, Yafei Zhang, Wensheng Li, Qing Zhan, Nan Wu

This preprint introduces PhasePlan, an ordered future-phase planning method for robot brain models. On a real-robot conveyor sorting task with a pretrained π0.5 backbone, PhasePlan reduces offline joint-action error by 22.5% relative to the original policy and reaches 92.5% per-position phase accuracy. By preserving where phase transitions occur within each 16-step action chunk, it may improve action timing in dynamic manipulation tasks.

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arXiv
SafetyExpert

Neuro-Symbolic Predicate Learning for Semantic Safe Robot Control

Zihan Ye, Jiayi Liu, Puze Liu, Jiayun Li, Georgia Chalvatzaki, Jan Peters, Kristian Kersting

This preprint introduces NEUPRO, a neuro-symbolic safety system that learns reusable visual safety predicates from human-written symbolic rules through a differentiable graph reasoner. On the REASON real-robot benchmark it reaches 92% mean safety-classification accuracy across seven tasks versus 55% for Qwen3.5 and 44% for DeepSeek-VL2, and it can explain violations in first-order logic. The result matters because it offers a path to interpretable, transferable safety constraints instead of opaque per-task cost functions.

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arXiv
Tactile ExplorationExpert

Tactile Curiosity Drives Robot Interaction

Klemens Iten, Alexander Proshkin, Bhavya Sukhija, Stelian Coros, Andreas Krause, Pieter Abbeel, Carmelo Sferrazza

This preprint presents TacEx, a framework that directs reinforcement-learning exploration by weighting tactile predictive uncertainty instead of total state uncertainty. In simulation, tactile-driven exploration produces contact-rich examples, emergent grasping without task rewards, and improved offline pick-and-place and frozen VLA post-training. It matters because it makes manipulation data collection and VLA adaptation far more sample-efficient by focusing learning where contact occurs.

RL
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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.