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机器人与具身智能领域的最新论文，以及每篇对产业的意义。

71 篇论文
主题全部操作 76VLA 62导航 35机器人综合 32机器人学习 27感知 22人形机器人 20仿真 15运动控制 13安全 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 1Faster on-device inference for flow-matching vision-language-action models 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 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 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 1
难度不限入门进阶专业
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arXiv
Aerial Manipulation专业

合成监督与场景图组合实现跨站点空中操作，MPAR对齐执行进度

Weixiang Guo, Rui Jin, Haotian Jin, Xinhang Xu, Ruiyang Liu, Haoran Zhao, Yi Wang, Weiqi Gai, Kun Cao, Lihua Xie

该预印本提出统一框架，将局部全身VLA行为组合为场景级空中操作。在仿真中完成21/50个跨站点任务（42.0%），物理平台验证了2/5次取水浇花任务。关键贡献包括无需真实平台演示的合成训练、MPAR对齐异步动作块，以及场景图引导的可行交接与拓扑转移。

VLA
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arXiv
Preference Learning专业

PrefPI：偏好引导策略迭代，让预训练机器人策略闯入分布外行为

Seungeun Rho, Wontaek Kim, Danfei Xu, Sehoon Ha

这篇arXiv预印本提出了PrefPI，一种仅用相对偏好反馈、迭代式引导预训练生成式机器人策略移出其初始有效支持范围的框架。它通过偏好条件生成建模与分类器无关引导（CFG）放大偏好信号，在真机上仅用150条偏好标注轨迹就将物体搬运高度从10.7厘米提升到19.8厘米。该方法为机器人行为对齐用户偏好提供了轻量级路径。

VLA
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arXiv
Real-time VLA专业

两阶段非均匀Flow去噪把VLA推理从61.6毫秒压到22.0毫秒，真机叠衣验证实时执行方法

Di Wu, Rongtian Shen, Ping Liu, Yan Shen, Zhenhan Yin, Shun Zuo, Xuhua Chen, He Zheng, Lingfeng Zhang, Jianglin Zhang, Tao Zhang

这是一篇arXiv预印本，尚未经过同行评审。作者以π0.5为基线，在双臂真机上测量了从相机、本体感知到命令响应的端到端延迟，并提出两阶段非均匀Flow Matching去噪，把推理步数从10步减到2步，模型推理时间从61.557毫秒降到21.956毫秒。在30次叠衣真机试验中，训练型方法Legato和免训练方法Temporal Smoothing表现最好；把两阶段去噪与这两种方法结合，推理成本大幅下降，但任务成功率有小幅回落。

VLA
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arXiv
Embodied policy专业

EWAM统一具身模型实现语义、视觉预测、动作的内部递进分工

Hao Wang, Jiajun Wen, Jingzhi Liu, Shuoshuo Xue, Zhiliang Chen, Min Lin, Yicheng Chang, Xiaoyu Guo, Yukang Zhuo, Zheng Chong, Yunshuang Nie, Jian Zhang, Weijia Liufu, Qingman Wu, Heming Xu, Bingchang Song, Dantong Wu, Zhiyuan Wang, Hang Xu, Jianhua Han

EWAM是一种以动作流为核心的一体化具身模型，通过非对称联合注意力让动作token在每层读取语义、当前视觉、预测未来和动作信息。在RoboTwin clean-to-random测试中达到72.1%，LIBERO平均98.8%，并在多个真实机器人上超过VLA、WAM和混合基线。该预印本显示统一训练可诱导从语义理解、视觉预测到动作生成的内部有序计算，且人类自我中心数据可提升跨具身迁移和真实场景鲁棒性。

VLA
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arXiv
VLA专业

MotorMind: Scaffolding General Vision Language Models for Zero-Shot Robot Manipulation

Bingxuan Li, Siqi Song, Yizhuo Wu, Jiarui Yao, Tong Zhang, Huan Zhang

Vision-language-action (VLA) models have advanced robotic manipulation, but their zero-shot generalization in new tasks and environments remains limited, and their reliance on specialized training keeps them from benefiting directly from rapidly advancing general-purpose vision-language models (VLMs). In parallel, recent agentic robotic systems leverage VLMs for high-level reasoning or coding agents for robot control, but often depend on extensive external models and tools, introducing additional complexity and cost.

VLA
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arXiv
VLA专业

WayFinder: Hierarchical Visual-Language-Action for Zero-Shot Waypoint Generation and Low-Level Kinematic Control

Timothy K Johnsen, Marco Levorato

Visual Language Action (VLA) models offer unprecedented generalization for autonomous robots; however, their real-world deployment is frequently bottlenecked by unreliable execution and the prohibitive computational cost of fine-tuning for specific robot embodiments and tasks. To bridge this gap, we propose WayFinder, an end-to-end, closed-loop hierarchical VLA framework that circumvents the need for fine-tuning by decoupling high-level task reasoning from low-level kinematic control.

VLA
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arXiv
VLA robustness专业

Self-Compensating VLA利用执行残差在线补偿机器人误差，RoboStress压力测试

Sohyun Lee, Yoonjae Baek, Jaesang Won, Jinnyeong Kim, Kang Hyunwoo, Seung-Hwan Baek, Ivan Laptev, Suha Kwak

这篇预印本提出自补偿VLA，在部署时利用指令与实际运动残差在线微调LoRA，让策略预补偿机器人执行误差。在RoboStress仿真基准和两台Piper机械臂上，该方法平均任务成功率相比基线提升超过30个百分点（真机），并在未见物体上保持提升。这为提升VLA在真实机器人上的长期可靠性提供了不依赖任务奖励的部署期适应方案。

VLA
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arXiv
VLA专业

Urgent Actions Go First: Urgency-Aware Denoising for Real-Time VLA Control

Zibo Wang, Haochen Han, Pengzhen Ren, Mingtong Dai, Fangming Liu

Diffusion and flow-matching Vision-Language-Action (VLA) policies generate action chunks through iterative denoising, incurring substantial inference latency that severely limits real-time robotic control. Existing acceleration methods treat an action chunk as a monolithic computational unit, ignoring a crucial physical reality of receding-horizon control: actions are generated jointly but consumed sequentially, resulting in inherently heterogeneous execution urgencies.

VLA
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arXiv
VLA专业

Faster and Better? Benchmark Bugs and Design Limitations Distort the Evaluation of Vision-Language-Action Acceleration

Qiwei Chen, Kaijun Zhou, Nuohui Shi, Zhiyang Li, Yuxuan Feng, Jinyu Gu

Simulated manipulation benchmarks are the standard tool for evaluating vision-language-action (VLA) policies and the acceleration methods that reduce their inference latency for on-robot deployment. On these benchmarks, we observe that some training-free acceleration methods, which approximate the baseline policy's computation, achieve higher measured success rates than the baseline itself.

VLA
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arXiv
VLA专业

RawVLA: Embodied Neural Image Signal Processor For Robotic Manipulation

Shuhong Liu, Heng Zhou, Lingfeng Qian, Yuhao Fang, Xianbao Hou, Qianyu Zhou, Lin Gu, Wei Sui, Jianfei Yang, Ziteng Cui

Vision-language-action (VLA) models typically operate on RGB images produced by a fixed camera image signal processor (ISP), leaving the imaging pipeline outside the learning and evaluation loop. We systematically examine the consequences of this overlooked design choice across five fundamental ISP dimensions: gain, sensor noise, chromatic response, tonal response, and bit depth.

VLA
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arXiv
VLA专业

Rho: A Foundation for Efficiently Adaptable VLA Models

Rho Team, Simran Bagaria, Daphne Chen, Dean Fortier, Jianlong Fu, Michael Harrison, Tess Hellebrekers, Neel Joshi, Andrey Kolobov, Dalton Moore, Galen Mullins, Michael Murray, Eduardo Salinas, Reuben Tan

General-purpose physical AI models must combine broad visual and linguistic capabilities with precise control across robot embodiments and efficient adaptation to downstream tasks. We introduce Rho, a family of open-weights VLA models for bimanual manipulation designed for data-light task adaptation on 3 embodiments representative of dual-arm robots across research labs and the industry -- YAM Box, UR AI Trainer, and FR3 Duo.

VLA
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主题页

操作 8VLA 4World Action Models 5World-action model 3Aerial manipulation 3Dexterous manipulation 3Sim-to-Real Manipulation 3Social Navigation 3

论文来自 arXiv 机器人领域。尚未生成摘要的论文，显示摘要开头部分。