MEKAWIRE
研究

論文

ロボティクスとフィジカルAIの最新論文と、それぞれが産業にもたらす意味。

論文60本
テーマすべてマニピュレーション 76VLA 65ナビゲーション 35ロボティクス全般 32ロボット学習 27知覚 22ヒューマノイド 21シミュレーション 15歩行移動 13安全性 11World Action Models 4World-action model 3Aerial 2Aerial 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 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 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 1Federated 3D Perception 1Fisheye detection 1Floorplan SLAM 1Geometry-aware manipulation 1Grasping 1Grounding 1Humanoid Control 1Humanoid RL 1Humanoid Tool Use 1Humanoid loco-manipulation 1Humanoid safety 1Humanoid whole-body control 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 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 models 1World-Action Models 1
難易度指定なし入門中級上級
日本語版は未提供のため、英語原文で表示しています。
技術「VLA(視覚言語行動)モデル」で絞り込み中、解除
arXiv
VLA上級

Graph-Reweighted Behavior Cloning with Human Interventions for Precise Robotic Manipulation

Xiaoman Li, Zhiqiang Yang, Jiayu Hu, Jipeng Kong, Yang Zhang, Jingwei Zhang, Fangzheng Yan, Chunhua Shen, Chenjia Bai

Long-horizon precise manipulation produces mixed-quality data for post-training vision-language-action (VLA) models: even successful demonstrations and intervention-assisted rollouts may contain detours, hesitations, failed attempts, and imperfect recoveries. The key challenge is to identify which action chunks actually advance the task, rather than uniformly cloning or discarding imperfect trajectories.

VLA
論文全文を読む（PDF）
arXiv
VLA上級

ObjDream: Guiding Vision-Language-Action Models with Object-Level Understanding and Future State Prediction

Huailiang Ma, Yuxuan Li, Ziyi Zhao, Xinkai Wang, Mingyu Li, Xinyu Guo, Lifeng Zhu, Aiguo Song, Cewu Lu, Jianhua Sun

Vision-language-action (VLA) models have emerged as a powerful paradigm for robotic manipulation, enabling robots to generate actions from visual observations and language instructions. However, most existing VLA models still operate on image-level visual representations inherited from pretrained vision-language backbones, while lacking fine-grained object understanding for manipulation.

VLA
論文全文を読む（PDF）
arXiv
VLA上級

Robota-Embed: Unified VLA Embeddings for Robot In-Context Learning and Evolution

Yunze Liu, Jing Wang, Shenrui Wu, Wenkun He, Guanqi Zhan, Xianzheng Ma, Junyu Chen, Wenhang Ge, Haozhan Tang, Yuchao Gu, Zhuoyang Zhang, Zekai Wang, Dantong Niu, Zhen Dong, Yuke Zhu, Trevor Darrell, Linxi "Jim" Fan, Song Han, Han Cai

Retrieving useful context for a robot requires matching its task, execution stage, and the continuation that should follow. We introduce Robota-Embed, to our knowledge the first vision-language-action (VLA) embedding model to jointly encode all three modalities in both current-state queries and recorded future continuations.

VLA
論文全文を読む（PDF）
arXiv
VLA上級

AdaRSI: On-Demand Recursive Self-Improvement for Embodied Agents

Peidong Wang, Ying Chang, Zhiming Ma, Xiaocui Yang, Shi Feng, Daqi Liu, Yuzhe Yang, Xu Cao, Chuang Wang, Jian Su, Daling Wang

Pretrained vision-language-action models (VLAs) often fail midway through unseen long-horizon tasks. Recursive self-improvement (RSI) offers a remedy, but embodied RSI methods either refine the harness around a frozen executor and so cannot add a missing motion, or train the executor on persistent failures without asking whether a failure lies in how the executor is called or in what it can do.

VLA
論文全文を読む（PDF）
arXiv
VLA上級

What Matters for World Modeling in Vision-Language-Action Models?

Qisen Ma, Yixiang Chen, Yuan Xu, Peiyan Li, Jiabing Yang, Kai Wang, Jianhua Yang, Jianlou Si, Jun Huang, Jing Liu, Nianfeng Liu, Yan Huang, Liang Wang

Integrating world models into Vision-Language-Action models (VLAs) has emerged as a promising direction, enabling policies to anticipate future dynamics beyond reactive observation-to-action mapping. However, existing approaches vary simultaneously in their world representations, integration paradigms, and policy backbones, making it difficult to determine what truly matters for effective world model integration.

VLAワールドモデル
論文全文を読む（PDF）
arXiv
VLA上級

VLRLA: Deployment-Time Adaptation of Vision-Language-Action Models through In-Context Reinforcement Learning

Diyuan Shi, Yiqi Tang, Hongyin Zhang, You Wu, Donglin Wang

Vision-language-action (VLA) models connect visual perception and language understanding with robot control, providing a foundation for instruction-conditioned manipulation. However, conventional pretrained VLA policies lack a mechanism for gradual adaptation through accumulated interaction experience during deployment.

VLARL
Foundation
論文全文を読む（PDF）
arXiv
VLA上級

ACRO: Actor-Critic Rollout Orchestration

Junhoo Lee, Seungyeon Kim, Baekseung Kim, Minkyu Kim, Suhyun Jeon, Jimyeong Kim, Nojun Kwak

Executing a vision-language-action (VLA) policy involves deciding when to continue an attempt and when to intervene to improve its chances of success. We introduce ACRO, an Actor--Critic rollout orchestration layer that makes these decisions for a fixed VLA.

VLA
論文全文を読む（PDF）
arXiv
VLA上級

Towards Agentic Robots: Model, Data, Environment, and Harness

Chonghan Qin, Dongming Wu, Kaituo Feng, Jiaxu Wang, Xinlei Yu, Peiwen Sun, Yijia Fan, Xilin He, Bingao Chen, Weitong Lian, Ryan Ji, Wenqi Ren, Xiaobin Hu, Dahua Lin, Yu-Gang Jiang, Xiangyu Yue

Foundation models are driving a paradigm shift in robotic intelligence, moving beyond low-level action prediction to goal-directed orchestration of data collection, model training, task planning, and real-world execution. We characterize this emerging paradigm as an agentic robots, in which agents reason over human instructions, interact with robotic environments, rectify execution errors autonomously, and dynamically adjust operational strategies.

VLAワールドモデル
Foundation
論文全文を読む（PDF）
arXiv
VLA上級

Active Generative Simulation: Meta-Learning What to Simulate for Sim-and-Real Policy Co-Training

Liqian Ma, Vaibhav Saxena, Utkarsh Aashu Mishra, Shuo Cheng, Yongxin Chen, Danfei Xu

Sim-and-real co-training is an effective way to scale robot learning by augmenting limited real-world demonstrations with simulation data. However, existing approaches typically treat simulators as fixed data sources, relying on uniform generation, offline retrieval, or manual parameter alignment by domain experts.

VLA
論文全文を読む（PDF）
arXiv
VLA上級

Human Demonstrations as Post-Training Data for Any Robot Body: Retargeting the Object, Not the Hand

Nagasrikanth Kallakuri

Human demonstrations are today used largely for pretraining vision-language-action models, with little ability to use the same data for target-embodiment or task-oriented training, which scalable deployment of robots in industrial and general environments depends on. A human demonstration recorded on a simple and scalable device such as a mobile phone can serve as post-training data for a robot whose body differs in structure and capability from the human who recorded it.

VLA
論文全文を読む（PDF）
arXiv
VLA上級

Coordi-pi: Coordination-Aware Dual-Hand Action Representation for Bimanual Dexterous Manipulation

Yiming Meng, Yanming Shao, Weijie Shen, Kunyang Lin, Jiayu Sun, Ping Luo, Yao Mu

Vision-language-action (VLA) models have shown strong potential for general-purpose robotic manipulation, but extending them to bimanual dexterous manipulation introduces a high-dimensional and structured action space. Existing bimanual VLA policies commonly concatenate right- and left-side arm--hand actions at each control timestep, leaving cross-hand interactions implicit within a single action vector.

VLA巧緻操作
論文全文を読む（PDF）
arXiv
VLA上級

One-shot Human Video-Guided Adaptation for Vision-Language-Action Models

Qi Chen, Yuwei Wu, Quan Chen, Kunpeng Gao, Jin Chen, Che Sun, Zhi Gao, Yunde Jia

Vision-language-action (VLA) models have recently achieved remarkable progress by leveraging large-scale robot datasets and foundation models for general-purpose robot manipulation. However, their ability to rapidly adapt to novel manipulation tasks beyond the training distribution remains limited.

VLA
Foundation
論文全文を読む（PDF）
arXiv
VLA上級

SafeDriveVLA: Navigation-Conditioned World Model Dreaming for Conflict-Aware End-to-End Autonomous Driving

Shaoyuan Xie, Zihan Zhang, Jingxuan Wang, Jiashu Qu, Xiaoqing Liang, Lingdong Kong, Junchi Lu, Henrik I. Christensen, Qi Alfred Chen

Vision-Language-Action (VLA) models are gaining attention in autonomous driving, inheriting commonsense priors from large language models (LLMs) and widening the navigation interface from a fixed command set to free-form natural-language instructions. However, this open-ended interface exposes a new vulnerability surface: an inattentive driver may issue instructions that are unsafe given the current scene, and a malicious user may issue dangerous ones on purpose.

VLAワールドモデル
論文全文を読む（PDF）
arXiv
VLA上級

When Do Discrete Actions Make Good Continuous Controllers?

Zeji Yi, Soham Dasgupta, Xinyang Zhang, Koushil Sreenath, Guannan Qu, Xi Chen, Max Simchowitz, Guanya Shi

Though modern AI models like LLMs predict discrete tokens, today's robotic foundation models represent actions as continuous vectors. In this paper, we challenge this common consensus by asking if continuous action parametrizations are superior to their discrete counterparts.

VLA
Foundation
論文全文を読む（PDF）
arXiv
VLA上級

Cracking Mobile Manipulation: From Failure Diagnosis to Agentic Robot System

Zixuan Wang, Jia-Ning Li, Yinzhou Tang, Yanzhao Yu, Biao Wu, Meng Fang, Xin Li, Yiwu Zhong

Recent vision-language-action (VLA) models have shown strong performance on local robot manipulation, yet their success does not readily translate to long-horizon mobile manipulation. In our experiments, a pretrained subtask VLA achieves 80% success rate on atomic manipulation tasks but only 19% when these skills are composed into mobile manipulation.

VLA
論文全文を読む（PDF）
arXiv
VLA上級

Paraphrase Consensus Is Not Self-Consistency: Instruction Reliance and Robustness in VLA Policies

Hanlong Li, Sihan Tan, Takeshi Ashizawa, Benjamin Yen, Kazuhiro Nakadai

Vision-language-action (VLA) policies fine-tuned on LIBERO are trained and evaluated on the same ten instruction strings, and rewording the instruction changes base OpenVLA's success by -38.0 points. Test-time prompt ensembling, which improves language models, does not recover this loss in any setting we ran.

VLA
論文全文を読む（PDF）
arXiv
VLA上級

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 Pang

Force feedback provides vision-language-action (VLA) policies with physical observations that can guide action selection and execution. However, differences in tasks, demonstrations, and control settings across existing systems hinder reliable comparisons of force integration designs.

VLA
論文全文を読む（PDF）
arXiv
VLA上級

MorphAct: Robot Policies Morphing with the Worlds They Act In

Ziyan Liu, Jiting Liu, Weihong Zhong, Wenda Yu, Yeqiu Chen, Jason Li, Xiaojiang Peng, Shuyuan Hu, Kai Wang, Bo Zhao

Progress toward general intelligence in the physical world requires scaling robot learning across diverse environments and interactions. However, existing robot policies use shared parameters that remain fixed across interaction contexts.

VLA
論文全文を読む（PDF）

テーマ別ページ

マニピュレーション 8VLA 4World Action Models 5World-action model 3Aerial manipulation 3Dexterous manipulation 3Sim-to-Real Manipulation 3Social Navigation 3

論文は arXiv のロボティクス分野から取得しています。当社の要約が未作成の場合は、要旨の冒頭を表示します。