MEKAWIRE
Research

Papers

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

14 papers
TopicAllManipulation · 101VLA · 73Navigation · 52General robotics · 41Robot learning · 32Humanoid · 24Simulation · 24Perception · 23Locomotion · 16Safety · 13Aerial · 4World Action Models · 4World-action model · 3Aerial manipulation · 2Dexterous manipulation · 2Sim-to-Real Manipulation · 2Social Navigation · 2Vision-Language Navigation · 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 · 1Benchmark for adding wrist force sensing to VLA manipulation policies · 1Bimanual Manipulation · 1Byzantine localization · 1Canal inspection · 1Co-speech Humanoid · 1Compositional Manipulation · 1Construction navigation · 1Contact-Rich Manipulation · 1Contact-rich Manipulation · 1Continual reinforcement learning on top of robot foundation models · 1Continuum sensing · 1Controlled comparison of how to add world models to VLA robot policies · 1Cross-embodiment transfer of humanoid whole-body controllers · 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 · 1Evaluating robot world models against real executions · 1Event-camera VO · 1Exoskeletons · 1Failure recovery · 1Faster on-device inference for flow-matching vision-language-action models · 1Federated 3D Perception · 1Fisheye detection · 1Floorplan SLAM · 1Force-aware robot foundation model pretrained on 2,000 hours of human tactile-glove data · 1Geometry-aware manipulation · 1Grasping · 1Grounding · 1Humanoid Control · 1Humanoid RL · 1Humanoid Tool Use · 1Humanoid loco-manipulation · 1Humanoid safety · 1Humanoid whole-body control · 1Humanoid whole-body training data from robot-specific video generation · 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 Optimization · 1Preference Learning · 1Quadruped locomotion · 1Real-time VLA · 1Real-to-sim · 1Reinforcement learning for industrial assembly · 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 Exploration · 1Tactile VLA · 1Temporal manipulation · 1Terrain traversability · 1Text-to-3D Policy · 1Tool Co-Design · 1Trajectory Correction · 1Traversability · 1Turning phone-recorded human demonstrations into per-robot post-training data by retargeting object motion · 1Two Unitree G1 humanoids playing table-tennis doubles with learned collision-risk filtering · 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 · 1Vision-language-action models without wrist cameras (learned foveation) · 1Visual Generalization · 1Visual Grounding · 1Visual Reward Models · 1Visuomotor Fusion · 1Visuotactile manipulation · 1World Models · 1World models · 1World-Action Models · 1humanoid learning from human video · 1robot foundation model training · 1robot learning from human video · 1world models for dexterous manipulation · 1
LevelAnyBeginnerIntermediateExpert
Filtered by technology vla · clear
arXiv
Vision-language-action models without wrist cameras (learned foveation)Intermediate

FoveaVLA: Foveation Actions Replace the Wrist Camera in VLAs

Bingao Chen, Lihan Zha, C. Karen Liu, Guy TevetStanford University; Princeton University

A Stanford and Princeton preprint shows a robot policy that drops the wrist camera and instead learns where to zoom in on a fixed external camera image, lifting real-world task success to 70.8% with one camera and 77.1% with two, against 52.1% for the same base model with a wrist camera. Fewer cameras on the arm means cheaper, sturdier robots and training data that could come from human videos recorded without wrist views.

Imitation LearningVLADepth VisionRobot Foundation Models
Physical IntelligenceFranka Robotics
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arXiv
Benchmark for adding wrist force sensing to VLA manipulation policiesIntermediate

ForceBench: Benchmarking Force Integration for Robotic Manipulation

Xuan Han, Zinuo Jin, Tianlin Li, Yunfan Lou, Xin Liu, Yuchen Xue, Yuanpeng He, Boxian Yao, Shuhan Zhan, Ruihai Wu, Zhibo PangPeking University (PKU-PI Lab); University of California, Berkeley; National University of Singapore; ePyBot Intelligence

Peking University researchers built a common test bed for the question of how a robot AI model should use a wrist force sensor, and found that the simplest option, writing the force reading into the model's text prompt, worked most consistently. On a real arm it lifted average task success from 40% to 63%, while fancier time-series designs hurt on assembly work.

Imitation LearningDexterous ManipulationVLATactile Sensing
Physical IntelligenceFranka Robotics
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arXiv
Faster on-device inference for flow-matching vision-language-action modelsIntermediate

Accelerating VLA Inference with Perturbation Informed Execution

Jehun Lee, Hajun Lee, Jimin Ryu, Youhan Lee, Beomseok Kang, Jonghyun Choi, Jae-Joon KimSeoul National University

A Seoul National University preprint makes robot foundation models act faster on embedded chips without retraining: it samples a few slightly perturbed action plans per call and executes more steps when the plans agree. On NVIDIA Jetson Orin and Thor this cut inference time by up to 2.3 times while success rates stayed level or rose, including 93.5% against 90.7% on a real UR10e arm.

VLAEdge AI ComputeRobot Foundation Models
NVIDIAPhysical IntelligenceUniversal Robots
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arXiv
Evaluating robot world models against real executionsIntermediate

WorldOracle: Benchmarking Action-Conditioned World Models against Real-World Executions

Haoyi Duan, Liliang Chen, Nan Wang, Yang Zhang, Yuhang Lu, Xiaotong Chen, Wei-Cheng Tseng, Shenyuan Gao, Zhaoshuo Li, Jingyi Jin, Pooya Jannaty, Dhruv Shah, Yuke Zhu, Ming-Yu Liu, Maoqing Yao, Guanghui RenAgiBot; NVIDIA; Princeton University; University of Texas at Austin; University of Toronto

AgiBot and NVIDIA researchers built a preprint benchmark that feeds robot world models the exact actions a real robot performed and checks whether the predicted video shows what really happened, including failed grasps and spills. Every one of seven models tested drifted on long runs, and the best rollouts of the robot arm often still got the physical outcome wrong, a warning for companies that plan to evaluate or train robots inside such models.

VLAWorld ModelsRobot Foundation Models
FoundationAgiBotNVIDIAPhysical Intelligence
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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
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
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
Multi-Robot ManipulationIntermediate

DuoMind: Enabling Distributed Multi-Robot Coordination with Semantic Communication

Hanchu Zhou, Dechen Gao, Hang Wang, Brendan Lynch, Boqi Zhao, Qiyao Ma, Raman Goyal, Junshan Zhang

In this preprint, the authors introduce DuoMind, a distributed multi-robot framework that pairs per-robot VLM orchestrators with low-level VLA action policies and structured semantic messages. Across two simulation benchmarks, it raises long-horizon success substantially over finetuned single policies, for example 39.25% vs 1.00% on Cook Pot and 78.00% vs 52.25% on Hang Bag. The work matters because it shows multi-robot coordination can be added to off-the-shelf foundation models without centralized control or joint multi-agent training.

VLA
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arXiv
VLA ConditioningIntermediate

Continuous Conditioning of VLAs with Augmenting EMG and Visual Task Descriptors

Edward W. Staley, Connor O. Pyles, Rahul Hingorani, Frank Camargo, Griffin Milsap, Jared Markowitz, Matthew S. Fifer, Michael Wolmetz

This preprint tunes action-expert vision-language-action models to accept non-language task cues: 8-channel EMG envelopes or visual segmentation annotations. On a real cube-picking task, EMG conditioning lifts cluttered success from 30% to 50% and cuts failure rate from 50% to 4%; on 16 simulated RoboCasa pick-and-place tasks, visual annotations lift cluttered success from 11% to 20% and cut failure from 47% to 25%. The findings suggest downstream VLA policies can usefully condition on continuous cues beyond fixed language prompts, especially in clutter.

VLA
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arXiv
VLA RobustnessIntermediate

Is Success All You Need? Investigating the Impact of Input Perturbations on VLA Behaviour in Tabletop Manipulation Tasks

Sophie Higham, Riccardo Andrea Izzo, Matteo Matteucci, Alessandro Suglia

This arXiv preprint introduces a benchmark-agnostic evaluation framework that measures how successful VLA-controlled trajectories change under input perturbations, going beyond task success rate. Testing three state-of-the-art VLA models across four LIBERO suites and seven perturbation conditions, it finds that perturbed trajectories can still succeed yet diverge substantially in motion smoothness, efficiency, and gripper behaviour. This matters because teams relying only on success rate may miss behavioural degradation that affects safety and reliability.

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
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
World-action modelIntermediate

Magic-W0: A Structured World-Action Foundation Model for Physical Intelligence

Xuhua Chen, Zhenhan Yin, Yuan Zhang, Lingfeng Zhang, He Zheng, Tong Mu, Shun Zuo, Dian Zhou, Di Wu, Xuan Zhou, Shaojie Wan, Rongtian Shen, Qiulong Xu, Yiduo Li, Yinglong Wang, Yanqian Wang, Kun Wang, Tao ZhangMagiclab Robotics Inc. (Magic-Lab Team)

Robot maker MagicLab has posted a preprint on Magic-W0, a 3.4-billion-parameter robot model that works out how the 3D scene will move and what it should look like afterwards while it is choosing an action. In the paper's October 3 revision it ranks first on the RoboDojo-Sim leaderboard with an average score of 36.75, only 0.48 points clear of the runner-up, and beats Physical Intelligence's π0.5 on five real-robot tasks by 94.6% to 91.8%.

Imitation LearningVLAWorld ModelsRobot Foundation Models
Physical IntelligenceMagicLabAgiBotNVIDIA
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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.

Imitation LearningVLARobot Foundation Models
Physical IntelligenceNVIDIA
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Topic pages

Manipulation · 101VLA · 73Navigation · 52General robotics · 41Robot learning · 32Humanoid · 24Simulation · 24Perception · 23Locomotion · 16Safety · 13Aerial · 4World Action Models · 5World-action model · 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.