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
Adaptive interventionIntermediate

UniIntervene++: An Adaptive Intervention Agent for Efficient Real-World Reinforcement Learning

Yudong Lin, Haoyuan Deng, Zhuoxuan Yuan, Zaijia Yang, Yuanjiang Xue, Ziwei Wang

UniIntervene++ is a preprint describing an online learned intervention agent that selects among the robot's own RL policy, trajectory correction, and a task-structured CodePolicy during real-robot reinforcement learning. Across five manipulation tasks on a UR7e arm it reaches 89.67% average success, 6 points above the best baseline, while cutting human-controlled steps to 0.77%. This could reduce the skilled-operator burden needed to make online robot learning practical.

RL
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arXiv
ManipulationIntermediate

Bridging Frontier Reasoning and Robot Execution: From Autonomous Demonstration Generation to Dense Language Supervision

Bosung Kim, Alexander Trevithick, Ruiyi Wang, Prithviraj Ammanabrolu

Preprint. The authors present two ways to connect slow frontier reasoning models to fast local robot policies: autonomous generation of corrective demonstrations for training, and dense language supervision at multiple granularities. They report that autonomous demonstration generation becomes more reliable and cheaper as successful examples accumulate, and that a deployment harness lets the frontier model guide the local policy. This matters because it could enable powerful but high-latency models to drive real-time robot manipulation.

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arXiv
Contact-rich ManipulationIntermediate

RATE: Risk-Aware Tactile Encoding for Contact-rich Robotic Manipulation

Yuyao Jiang, Haichao Liu, Jiarui Zheng, Zihan Ding, Weihao Yuan, Ziwei Wang

RATE (preprint) introduces a risk-aware tactile encoding method that learns task-conditioned contact risk from tactile history and alert supervision, then injects it into a frozen ACT policy through a lightweight residual adapter. It reports 71.8% average success on eight UniVTAC simulation tasks and 73.3% average success on three real-world tasks, outperforming baselines including 3.3B-5B parameter visuomotor policies while updating only about 0.59% of model parameters. This matters for contact-rich manipulation because similar tactile readings can hide very different failure risks.

Tactile Sensing
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arXiv
ManipulationIntermediate

AdaTempo: Learning Shared Relative Tempo from Demonstrations for Faster Robot Manipulation

Jiale Cao, Yike Niu, Zhengrong Xue, Huazhe Xu

This preprint presents AdaTempo, a self-supervised method that speeds up visuomotor imitation policies by extracting shared phase-relative tempo patterns from demonstrations and resampling trajectories before training. It achieves up to a 3.57x speedup over demonstrations, and in real-robot tests raises aggregate success from 58.3% with ACT to 77.8% while completing tasks 2.02x faster. The approach matters because it embeds faster execution directly into standard policies such as ACT or Diffusion Policy without runtime retiming or extra speed-selection modules.

Imitation Learning
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arXiv
VLA evaluationIntermediate

ManiPhysicsBench: Physics-Based Assessment of Object Preservation in VLA Manipulation

Sangwu Park, Yeonjun In, Wonjoong Kim, Sungwon Kim, Sein Kim, Chanyoung Park

This preprint introduces ManiPhysicsBench, a simulation-based benchmark that scores vision-language-action manipulation policies on object preservation, not just task completion. It combines a curated 91-object material property library with post-rollout physics-solver damage checks in LIBERO and SimplerEnv; public VLAs average 13.5% safe success versus 48.3% task success on one platform. The work matters because real-world deployment of fragile-object handling depends on not damaging goods, yet current benchmarks ignore this risk.

VLA
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arXiv
TraversabilityIntermediate

From Language Priors to Field Adaptation: Preference Learning for Traversability Estimation

Simon Schwaiger, David Seyser, Alessandro Scherl, Zlatan Ajanović, Wilfried Wöber, Gerald Steinbauer-Wagner

This preprint presents a traversability estimator that models terrain preferences as von Mises-Fisher prototypes in a frozen vision-language feature space, initialized from language rules and fine-tuned with sparse relative image annotations. It achieves competitive accuracy with end-to-end trained models on WayFAST, e.g., HDR 0.5 of 21.8% vs 22.5% for the SOTA, while improving sample efficiency and using only about 8.2k trainable parameters. This matters because it enables rapid domain adaptation for outdoor mobile robots without extensive per-platform annotation.

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arXiv
World modelsIntermediate

DeltaWorld: Physically Consistent Interactive World Simulators via Action-Conditioned Latent Increment Learning

Boyuan Hou, Xiaoge Cao, Chaofan Zhang, Shuo Wang, Shaowei Cui

DeltaWorld is a preprint describing an interactive world simulator for robot manipulation that predicts action-conditioned latent increments instead of full future latent states. On a self-collected cross-robot dataset it reduces FVD by 46.6% and LPIPS by 31.1% relative to the IWS baseline, and on the IWS benchmark it improves FID from 44.16 to 36.04. This matters because more stable long-horizon action-conditioned video prediction could reduce real-robot rollouts for policy training and evaluation.

World Models
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arXiv
Adaptive MPCIntermediate

LLA-MPC on Embedded Hardware: Rapid Adaptive Control with Thousands of Parallel Models

Henry Z. Liao, Maitham F. AL-Sunni, John M. Dolan

This preprint describes a generalized, learning-free adaptive model-predictive controller that runs thousands of candidate physics models in parallel on embedded hardware. On an F1TENTH car with low-friction tires, it completes high-speed tracking and changing-surface laps where a fixed nominal model crashes within a few laps. The work matters because it shows rapid online system identification and control can run on a small robot computer without pretraining or warm-up data.

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arXiv
Humanoid RLIntermediate

Beyond Reward Hacking: Proxy Divergence Across Four Layers of a Staged Humanoid Learning Pipeline

Arunabh Bora

This preprint introduces a four-layer 'proxy audit' for humanoid reinforcement-learning pipelines, treating reward terms, curriculum gates, evaluation metrics, and reference motions as failure-prone proxies. The authors demonstrate measured failures and corrections in a simulated 1.91 m humanoid trained with PPO across four stages and 13,500 iterations on one laptop GPU; among the results, an averaged-error curriculum gate advanced on schedule while the target skill was missing, and a phase-aliased push test rated 0.5 m/s as more dangerous than 2.0 m/s. The low-cost reformulations matter because they can make simulated robot training results more trustworthy without retraining.

Humanoid
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arXiv
Visuomotor FusionIntermediate

Register-Routed Delayed Fusion: Rewiring Shortcut-Prone Observation Fusion in Visuomotor Imitation

Jieting Long, Weidong Cai, Weiming Zhi

This arXiv preprint introduces Register-Routed Delayed Fusion (RRDF), a Transformer observation-fusion schedule for visuomotor imitation that blocks direct proprioception-to-vision attention and routes cross-modal information through learned registers. On three real-robot tasks, RRDF improves nominal success rates from 55%, 65%, and 50% for dense ACT to 100%, 100%, and 85%; under held-out object positions, it improves from 30%, 30%, and 0% to 95%, 100%, and 20%. The policy also shows lower state-to-image action sensitivity, suggesting more robust visual responsiveness in imitation-learned manipulation.

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arXiv
Object Pose EstimationIntermediate

RYOPO: Bringing End-to-End Category-Level Object Pose Estimation into Real Time

Hakjin Lee, Junghoon Seo, Jaehoon Sim

RYOPO is an end-to-end RGB-D model that jointly detects, segments, and estimates 9-DoF category-level object poses without CAD shape priors or an external instance segmentor. On NOCS REAL275 it reaches 52.5 AP at 5°5cm in joint evaluation and 51.1 AP in all-object evaluation, while running at 31.8 FPS on an RTX A6000. This is a preprint; the speed-accuracy balance matters for real-time manipulation where crop-based pipelines scale poorly with object count.

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arXiv
Laboratory ManipulationIntermediate

RoboChemGym: A Protocol-Driven Generative Simulation Framework for Long-Horizon Chemical Manipulation

Chenxi Li, Haiyuan Wan, Rui Li, Jingyuan Li, Sha Zhang, Bohan Feng, Jianbao Cao, Zhangrui Zhao, Di Hu, Wangmeng Zuo, Shixiang Tang, Minting Pan, Dongzhan Zhou

Preprint. The authors built RoboChemGym, a simulation framework that turns written chemistry protocols into long-horizon robot manipulation scenes and expert trajectories, with iterative action/scene refinement and domain randomization. In simulation, generation succeeds on 89.2% of 3-step, 73.4% of 7-step, and 52.3% of 11-step clean protocol tasks; adding 1,000 synthetic trajectories to 10 real demonstrations raised real-robot policy success by about 22–24 points over real-only training. This matters as a scalable data source for autonomous lab robotics.

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arXiv
VLAIntermediate

MixVLA: Adaptive Mixing of Non-Invariant Information for Generalizable Vision-Language-Action Models

Pingrui Zhang, Yu Zhang, Pengyuan Wu, Bin Wang, Haoming Song, Xianqiang Gao, ZhaxiZhuoma, Zhigang Wang, Dong Wang, Bin Zhao, Xuelong Li

This preprint introduces MixVLA, a model-agnostic training strategy for vision-language-action (VLA) policies that separates invariant and non-invariant latent representations, then adaptively mixes the non-invariant branch to regularize environment-specific variation. On LIBERO-Plus it posts a 76.2% average zero-shot success rate versus 69.6% for OpenVLA-OFT, and on RoboTwin it lifts π0.5 clean-to-randomized success from 46.0% to 62.4%. Because it needs no extra out-of-distribution data or architecture changes, it is a practical robustness upgrade for existing VLA stacks.

VLA
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arXiv
Vision-Language NavigationIntermediate

GeoScaffold: Learning Compact Geometric Latents via Reconstruction for Efficient Vision-Language Navigation

Yixuan Jiang, Wentong Li, An Liu, Zihao Xin, Fulin Tang, Cong Leng, Yang Gao, Jian Cheng

This preprint presents GeoScaffold, a training-only geometric supervision framework for streaming vision-language navigation. A 4B policy fine-tuned with eight geometry query tokens reaches 64.8% success rate on R2R-CE and 59.6% on RxR-CE while using only RGB at inference, and runs at 81 ms per frame on an RTX 4090. The approach avoids resident depth sensors or 3D encoders, making spatially aware navigation cheaper to deploy on edge robots.

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arXiv
Curriculum RLIntermediate

Bidirectional Voronoi-biased Exploration Curriculum for Reinforcement Learning

Juri Pfammatter, Kaixian Qu, Clemens Schwarke, Victor Klemm, Marco Hutter

This preprint introduces BVER, a bidirectional curriculum that grows start states from the goal and goals from the initial state distribution, biased toward unexplored task space and steered together RRT-style. On quadruped box climbing it reaches 95% success on a 0.4 m box in about 65% fewer iterations than the best reference-free baseline and is the only reference-free method tested to climb a 0.7 m box. It matters because it removes the need for demonstrations or hand-designed rewards in sparse-reward robot learning.

RL
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arXiv
Sim-to-real manipulationIntermediate

SARI: Phase-Split Sim-Real Co-Training for Contact-Rich Manipulation

Xingxin He, Yuxuan Jiang, Haonan Zhang, Chuhan Cui, Kaile Li, Zhongxing Zheng, Caihao Xu, Ziqi Wang

This preprint introduces SARI, a phase-split co-training framework that post-trains a vision-language-action policy on simulated free-space approaches and real contact interactions for contact-rich manipulation. On a lamp task, it reduces real demonstration time by 34.3% and reaches 27.5% success at unseen object placements, where full-task simulation, real-only, and full-task co-training baselines score 0%. The result shows how to allocate scarce real contact data while preserving spatial generalization.

VLA
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arXiv
Fisheye detectionIntermediate

FUSEye: Training-Light Fisheye Detection with Overlapping Views and Zero-Initialized Adapters

Wenya Su, Kai Luo, Di Wen, Ruiping Liu, Yufan Chen, Junwei Zheng, Kunyu Peng, Kailun Yang

This preprint introduces FUSEye, a training-light method that adapts a frozen COCO-pretrained YOLO26-x detector to fisheye camera images using overlapping views, zero-initialized residual adapters, and learned cross-view fusion. On WoodScape, it lifts mAP50 from 14.80 to 26.61, reaching 84.3% of full fine-tuning accuracy while training only about 227k added parameters. This matters because it lets robots adapt off-the-shelf detectors to wide-angle cameras without expensive full retraining or calibration.

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arXiv
Object SearchIntermediate

LOCUS: Landmark-Oriented Container Discrimination Using Spatial Graphs

Taylor Bergeron, Shibani Senthilbabu, Kevin Leahy

This preprint presents LOCUS, a graph neural network that updates CLIP embeddings with spatial context from a scene graph to rank same-class containers when searching for occluded objects. In iTHOR simulation, it reduces expected containers opened per search to 0.257 of all containers, a 24% improvement over TidyBot, and transfers zero-shot to RoboCasa and a Stretch 3 real-kitchen pilot. It matters because real robots must choose among visually identical drawers or cabinets, which existing class-level methods cannot do.

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arXiv
Construction navigationIntermediate

CORNAV: Construction-Aware Reasoning for Robot Navigation on Active Worksites

Parastoo Ali Pour, Deepak Prakash Kumar, Tommy Zhou, Pramod Khargonekar, Mohammad Abdullah Al Faruque

This preprint presents CORNAV, a navigation system that lets robots operate on active construction sites by combining 2D CAD blueprints, project schedules, open-vocabulary 3D scene graphs, and LLM safety checks. In ablation tests, blueprint grounding raised task success from 13.0% to 72.2% and schedule awareness eliminated hard-zone violations, while safety validation cut hazardous-zone entry from 100% to 0% for mislabeled hazards. The approach matters because it avoids the need for BIM models and lets robots follow site safety constraints that change over time.

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arXiv
Dexterous ManipulationIntermediate

DexJoCo-X: Benchmarking Action Representations for Multi-Hand Dexterous Manipulation

Xiangwei Jiang, Yao Mu, Lixin Duan, Wen Li

This preprint introduces DexJoCo-X, a simulation benchmark and toolkit for comparing action representations across seven dexterous hands and six manipulation tasks using 2,100 balanced demonstrations. Under a single joint policy, function-aligned action slots reach 47.7% mean success over all 42 hand-task pairs, versus 47.0% for native joint coordinates and 33.1% for learned cross-hand latents. The work matters because it isolates representation choices under matched data and training, helping teams build scalable cross-embodiment dexterous policies.

Dexterous Manipulation
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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.