MEKAWIRE
Research

Papers

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

276 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
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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arXiv
Adaptive action horizonsExpert

SplineWAM: Adaptive Action Horizons for World Action Models via B-Spline Representations

Jun Guo, Xiaoshen Han, Qiwei Li, Nan Sun, Peiyan Li, Heyun Wang, Hang Lai, Weinan Zhang, Xinghang Li, Huaping Liu

This preprint introduces SplineWAM, which replaces the fixed-length action chunk of a world action model with a fixed-size window of cubic B-spline knot times and control points, letting each prediction adapt its temporal horizon and resolution. On LIBERO-Plus and RoboCasa it improves success rate by 8.2 and 4.4 points over an action-chunking WAM while issuing 22% and 26% fewer policy calls per episode; on three real bimanual tasks it matches or beats the baseline while decoding 1.2–1.6× more executed motion per call. This matters for cloud-served robot fleets because fewer calls per task directly increase throughput.

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arXiv
MAPFExpert

Belief-Aware Multi-Agent Path Finding under Map Uncertainty

Viraj Parimi, Shao-Hung Chan, Han Zhang, Jingkai Chen, Brian Williams

This arXiv preprint describes MAGIC, a framework that adds online spatial belief inference to multi-agent path finding under unknown map obstacles. On standard MAPF benchmarks it reduces executed sum of costs on 96.3% of instances and scales to 800 agents. The work is relevant for warehouse robot fleets that must route around fallen pallets, spills, or other local map discrepancies without building expensive contingent plans.

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arXiv
Canal inspectionBeginner

Non-Invasive Inspection of Water Canals Using Dronar

Michael Zielinski, Zhizhan Wang, Benjamin Dymond, Reza Razavian, Zhongwang Dou

This arXiv preprint presents dronar, a custom unmanned surface vehicle that carries a consumer Lowrance sonar unit to inspect concrete water canals without draining them. In Arizona Canal field tests, the system detected sediment accumulation as depth offsets up to 15 cm versus a cleaned segment, with repeated clean runs agreeing within 7 cm. It offers utilities a lower-cost way to prioritize canal maintenance between the current four-year dry-up cycles.

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arXiv
UAV NavigationExpert

DiffWAM: A Fast and Efficient Navigation World Action Model

Mo Zhu, Yuze Wu, Xijie Huang, Xiao Cui, Fei Gao, Xin Zhou

DiffWAM is a preprint that introduces a UAV navigation world-action model. It recovers camera trajectories directly from the predictive features of a frozen video model, avoiding future-video decoding and multi-frame geometric reconstruction at deployment. The paper reports a trajectory RMSE of 0.3492 m and adds FastDreamer, an execution system that overlaps computation with flight for continuous UAV control.

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arXiv
VLAExpert

When Instructions Retrieve Trajectories: Diagnosing and Mitigating Generalization Failures in VLA Models

Hung-Jen Chen, Yu-Hsun Hou, Yan-Hong Chen, Yan-Fu Chen, Binghua Cai, Min Sun, Chun-Yi Lee

This preprint identifies a failure mode in vision-language-action (VLA) policies where instructions retrieve familiar trajectory families but do not bind language to scene-dependent action choices, so robots can execute a clean but wrong task. It introduces Equivariant Counterfactual Training (ECT), a paired data and loss scheme that supplies same-instruction demonstrations with different scenes and trains counterparts jointly. In controlled LIBERO-PRO tests, mean position-swap success rises from 36% to 59%, and on a real UR5e, unseen-position success rises from 3/40 to 35/40, showing the approach reduces counterfactual task errors.

VLA
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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
Submovement decompositionExpert

Identifiable Decomposition of Submovements in Human Hand Trajectories

Adrian Prados, James Hermus, Ramon Barber, Sylvain Calinon

The authors present Sub-ID, a submovement decomposition method for human hand trajectories that uses a spatiotemporal kernel correlation as an identifiability criterion, and it is described as a preprint. On synthetic tests with 3, 8, and 15 overlapping primitives it recovers ground-truth parameter distributions where baselines degrade or fail, holding position error to 0.012 m at K=15 and keeping critical primitive overlaps to about 3%. This gives robotics and motor-control researchers a practical way to extract physiologically grounded motion primitives from long, three-dimensional movements.

Imitation Learning
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arXiv
Egocentric dataExpert

Ego4WAM: What Matters When Scaling Egocentric Human Data for Robot Learning?

Zhihao Sun, Liu Liu, Xinjiang Wang, Haoyi Jiang, Wei Feng, Huiqiang Zhang, Xiaosong Jia, Zhizhong Su, Zuxuan Wu

Preprint. The paper presents Ego4WAM, a controlled study of how to scale and stage egocentric human data for bimanual robot manipulation under a world-action model. Its headline result is that video-only world-model pre-training on 15K hours of egocentric video plus only about 0.1K hours of robot data reaches a 20.29 average RoboDojo score and 14.35% success rate, roughly matching or exceeding several models trained with far more robot data. The work matters because it identifies which data properties—human-robot alignment, duration, task diversity, and supervision—actually improve closed-loop robot generalization.

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arXiv
Continuum sensingExpert

Magnetic based In-situ Self 3D Pose Estimation for a Modular Soft Tendon-Driven Continuum Robot via IMU-Fusion

Zheng Cao, Guo Ning Sue, Xiangyun Bu, David Quinn, Junzhe Hu, Carmel Majidi

This arXiv preprint introduces a camera-free shape-sensing system for a modular tendon-driven continuum robot. Onboard IMUs and PCB-coil magnetic fields are fused through a learned joint model, producing 16.7 Hz backbone updates and pooled 10.0 mm backbone RMSE across 20 real-robot trials. It matters because it enables closed-loop control of soft manipulators when external vision is unavailable or occluded.

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arXiv
ManipulationExpert

Dream4ACT: A Shared Visual Action Interface for Multi-Embodiment Video-Action Modeling

Xiangyu Zhu, Jin Xu, Yue Guo, Xin Wu, Yifan Sun, Xiancong Ren, Jianxin Sun, Yong Dai, Xiaozhu Ju

Dream4ACT is a preprint video-action world model that turns target robot joint configurations into four fixed virtual-camera 'action views' rendered from URDF models, so a single model can control different robot arms without per-robot action heads. On RoboTwin 2.0 it averages 88.98% success, scores 65.66 on TriWorldBench, and on two common real-world tasks averages 85.0–90.0% across four robot platforms. It matters because the shared visual action interface removes physical-camera calibration for action encoding and uses training-free joint recovery, simplifying multi-embodiment manipulation models.

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arXiv
Soft Pneumatic ActuatorsExpert

TO-mdiSPAs: Topology Optimization of multi-directional Soft Pneumatic Actuators

Swagatam Islam Sarkar, Prabhat Kumar

This preprint introduces a topology optimization method for designing multi-directional soft pneumatic actuators using Darcy-law pressure modeling and a robust three-field formulation. Optimized chambers are assembled into a simulated three-arm gripper; Abaqus tests at 0.15 MPa show single-arm tip displacements up to about 15.8 mm, with bending directions tracking the vector sum of activated chamber pressures. If experimentally validated, it could replace heuristic soft actuator design with systematic multi-axis bending for soft grippers.

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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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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.