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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。

15 篇论文
主题全部Manipulation 41VLA 28Navigation 16Perception 16Learning 13Robotics 13Humanoid 12Safety 7Simulation 7Locomotion 5World Action Models 4Aerial 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 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
难度不限入门进阶专业
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arXiv
Learning专业

Directional Pullback Regularization for Long-Horizon Rollout of Latent World Models in Autonomous Driving

Shatadal Mishra, Ahmadreza Moradipari, Nejib Ammar

Learned world models for autonomous driving operate in unconstrained latent spaces, yet the physical systems they represent are governed by strict non-holonomic constraints: ground vehicles cannot realize instantaneous pure lateral motion. We study long-horizon latent rollout modeling for autonomous driving and ask whether rollout quality can be improved by assigning a motion-aware geometry to latent transitions, rather than by redesigning the predictor architecture.

世界模型
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arXiv
Simulation专业

StreamingWAM: Streaming World Action Modeling via Chunk-Wise Staircase Flow Matching

Shaojie Ren, Jingwen Sun, Zezhi Liu, Hongjin Chen, Xin Jin, Wei Zou, Zhibo Chen

World Action Models (WAMs), often built upon pretrained video-generation world models, have emerged as a promising framework for general-purpose and open-ended robot control. However, their real-world deployment often suffers from discontinuous and jerky execution.

世界模型
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arXiv
Learning专业

LeHami: Symplectic Latent Dynamics for World Models

Romina Yalovetzky, Randall Balestriero, Yann LeCun

Joint-embedding predictive architectures (JEPAs) learn world models directly in latent space, but unconstrained autoregressive predictors may contract the latent distribution over long rollouts. We introduce Symplectic Hamiltonian JEPA (LeHami), which replaces the standard predictor with an action-conditioned Hamiltonian flow over a structured latent state (q, p) integrated using an exactly symplectic leapfrog step.

世界模型
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arXiv
Manipulation专业

Grounded-Dreamer: Robot Learning from World Model Synthetic Data with Physical Grounding

Feng Jiang, Shu Chen, Yuanfei Wang, Yuan Li, Zhenhao Shen, Jiaqi Liang, Yang Chen, Lei Wei, Chen Xie, Ruihai Wu

Scaling robot manipulation policies requires large amounts of diverse, action-labeled data, yet collecting such data in the real world remains expensive and difficult to scale. Existing methods either depend on human teleoperation, rely on simulation with limited visual realism, or use human videos that lack embodiment-specific annotations.

遥操作世界模型
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arXiv
Learning专业

Dream to Remember: Continual Steering of Generalist Robot Policies with World Models

Bahey Tharwat, Iman Nematollahi, Marvin Alles, Xingyuan Zhang, Akshay L Chandra, Abhinav Valada

Generalist robot policies are trained with imitation learning and are bounded by the quality and coverage of their demonstrations. Reinforcement learning can improve and extend them, either by fine-tuning the policy weights or by training a small module to steer the input noise of a frozen generative policy.

模仿学习世界模型RL
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arXiv
Manipulation专业

Beyond Successful Robot Demonstrations: Learning from Human Interactions and Modeling Failure Dynamics

Peiyan Tu, Cong Wang, Hanxin Zhu, Xin Jin, Zhibo Chen

Action-conditioned world models offer a promising foundation for generalist robotic intelligence by predicting the consequences of actions. However, existing robotic datasets provide limited coverage of the diverse interaction dynamics required for generalizable world modeling.

世界模型
Foundation
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arXiv
World models进阶

DeltaWorld：动作条件潜变量增量学习让机器人世界模型更物理一致

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

论文提出DeltaWorld，一种用于机器人操作的物理一致交互式世界模拟器。其Delta-LTM预测动作引起的潜变量增量并叠加到当前状态，Interaction-aware Latent Alignment强化机器人-物体交互区域的监督。在IWS基准和自我采集的跨机器人数据集上，DeltaWorld相比IWS基线将FVD降低46.6%，LPIPS降低31.1%。该研究为预印本，尚未经同行评审。

世界模型
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arXiv
JEPA World Models专业

AVL-JEPA：防止JEPA世界模型因果动态信息坍缩，提升机器人视觉扰动下控制成功率

Yikang Qiao, Ling Zhang, Ziying Song, Duan Huang

本文是预印本，尚未经过同行评审。作者提出AVL-JEPA方法，通过动作锚定和视觉不变性约束，防止联合嵌入预测架构世界模型丢失与动作相关的物理动态信息。在TwoRoom、PushT等四个仿真机器人控制任务中，AVL-JEPA显著提升视觉扰动下的成功率（如PushT高斯噪声下从15.3%升至81.5%），同时保持清洁环境性能，表明该方法能有效保留因果动态信息。

世界模型
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arXiv
Video world models进阶

PhysicsLENS 揭示视频生成模型“物理属性盲区”：看似合理实则忽略隐藏物理条件

Isaiah Milkey, Som Sagar, Aditya Taparia, Xinyuan Liu, Jiqing Wen, Ransalu Senanayake

PhysicsLENS 是一个新基准，通过匹配场景对评测机器人视频生成模型是否遵循文本指定的隐藏物理属性。在 4 个模型生成的 439 个视频中，47 个被人工评定为物理合理，其中 34 个（72%）未遵循指定属性；同一模型在隐藏属性条件下物理合理性平均下降 0.28 分，但不显著。该工作说明视觉合理性与属性遵循必须分开评估，对机器人世界模型可靠性有重要意义。本文为 arXiv 预印本，未经同行评审。

世界模型
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arXiv
Model-based RL专业

PL-MPC 在模型预测控制中同时改进 critic 监督、终端估计与策略蒸馏，真机 unseen 尺寸成功率超 TD-M(PC)2

Kowndinya Boyalakuntla, Yuhan Liu, Abdeslam Boularias

PL-MPC 是 arXiv 预印本，未注明经同行评审。该方法在 TD-M(PC)2 上同步修改 critic 监督、MPC 终端值估计和 planner 到策略的转移，不改世界模型与 MPPI。在 HumanoidBench 的 balance-hard 上 TAR 从 98±18 升至 387±255，hurdle 从 199±13 升至 466±200；在 KUKA IIWA14 真机 wrench–nut 对齐中，训练尺寸和两个未见尺寸的观测成功率均高于 TD-M(PC)2。

世界模型
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arXiv
Social Navigation进阶

Social-WM：用可执行动作差异做安全社交导航的隐世界模型

Zhihao Zheng, Mooi Choo Chuah

Social-WM 是一个使用 RGB 视频离线训练的隐世界模型规划框架，区分名义动作与实际可执行动作。在 Social-HM3D 上，它取得 63.77% 成功率，行人碰撞率降至 21.67%，比 NavThinker 相对降低 44.6%；零样本迁移到 Social-MP3D 仍保持 54.15% 成功率和 23.34% 碰撞率。该工作为未同行评审的预印本。

世界模型
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arXiv
Manipulation进阶

RoboCoach 让世界模型主动当教练：想象失败后定向补数据，真机成功率升至 75.0% 和 83.8%

Jiajun Liu, Yifan Chen, Yichao Liu, Jiayi Zhang, Ruoqu Chen, Shaoxuan Xie, Guocai Yao, Mengdi Xu, Sen Cui, Changshui Zhang

RoboCoach 是一个预印本框架，用世界模型中的想象执行来诊断长程机器人任务中最先失败的子任务，并据此请求对应技能专家的演示数据。在 Franka 和 AgileX 真机上，仅追加 150 条子任务演示，成功率分别从 13.3% 升至 75.0%、从 40.0% 升至 83.8%，显著超过统一采集加共享适配器的基线。该工作表明世界模型可以成为主动教练，把稀缺的真实数据导向可复用的技能模块。

世界模型
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arXiv
Learning专业

Anisotropic Representations Improve Planning in JEPA World Models

Mingu Kang, Yoori Oh, Sookyung Kim, Joonseok Lee

Latent world models learn action-conditioned dynamics in representation space and often score candidate actions by Euclidean distance to a goal representation. Joint training typically regularizes the representation to prevent collapse, but the resulting representation geometry also determines how terminal errors are weighted during planning.

世界模型
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主题页

操作 8VLA 4World Action Models 5Aerial 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。