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。
3D semantic occupancy prediction is crucial for autonomous driving, but camera-based methods degrade in low-light conditions and LiDAR-based methods are sensitive to adverse weather. 4D radar is more robust, yet its sparsity and the high cost of radar tensor processing limit its use.
Utility-scale photovoltaic (PV) inspection is dominated by aerial surveys, which are effective for identifying module-level hot cells but provide limited visibility of critical components located under the array, including connectors, cabling, fasteners and structural elements. These assets are difficult to inspect consistently at scale, and their defects are often spatially small, partially occluded and difficult to localise from isolated images.
Sonar was one of the first sensors with which mobile robots built maps, and in-air sonar keeps working where cameras and lidar fail, in darkness, smoke, dust and next to glass. Classical sonar SLAM, however, relied on geometric features or occupancy grids built from sparse range readings, and both struggle with the specular reflections that dominate indoor sonar.
Generalist visual-navigation policies tell a robot which direction to go but not how far. A separate lifting step is needed at deployment to convert their unitless trajectory into a metric path that a controller can track; existing scalar lifters apply uniform scaling that misses per-waypoint corrections, while in-policy approaches that inject monocular depth into the policy inherit the prediction errors of the depth model near obstacles and at close range.
Launching a micro unmanned aerial vehicle (UAV) from a hovering carrier and recovering it in flight requires reliable confirmation that the child is securely seated before the carrier is allowed to descend. This is challenging because vision provides relative guidance but does not directly indicate load transfer, and a downward-facing camera can lose its fiducial near touchdown.
Quadruped robots deployed for inspection and field missions must cross heterogeneous terrain, from stairs, slopes, and gaps to high platforms, stepping stones, and balance beams, where safe support is sparse or see-through. Existing methods interpose a terrain map or a depth image between sensing and control.
Light Detection and Ranging (LiDAR) is critical for autonomous perception, yet its performance is known to degrade in adverse weather. While existing literature models snow as atmospheric noise that degrades performance, our granular evaluation on the WADS-3D and CADC datasets reveals a counter-intuitive phenomenon: detection accuracy consistently improves as snow intensity increases.
LiDAR-inertial odometry (LIO) is widely used for real-time localization and mapping, but its reliability can degrade substantially in subterranean environments affected by smoke, dust, and other airborne obscurants, whereas radar remains comparatively robust. This article presents a one-class temporal learning framework for LiDAR degradation detection and reliability-aware localization under such conditions.
Nearly every autonomous or semi-autonomous flipper controller assumes access to an elevation map of the terrain under and in front of the robot. The terrain beneath the chassis is occluded in the sensors, so the map is remembered through odometry integration, whose drift corrupts it.
本文报告一种预印本方法 DR-IPC，它结合 EKF 状态滤波与非线性扰动观测器，将扰动估计注入 NMPC 预测，联合优化局部避障轨迹和控制量。在 Gazebo 多目标导航中，DR-IPC 在有风扰和悬挂载荷时将任务完成率从 1/10 提高到 9/10；室内拱门实验中高度 RMSE 从 0.34 m 降至 0.01 m。该方法尚未经过同行评审，代码计划开源但尚未发布。
这篇预印本提出 GlassGuard 框架，结合视觉玻璃分割、LiDAR 结构线索和深度无关的2D投影几何验证，重建建筑玻璃平面并纳入导航地图。在九个真实场景、2.1公里机器人运行中，全景版本实现85.2%的玻璃覆盖率，同时每帧假阳性体素仅15.3个，显著优于基线。该方法在保证玻璃碰撞边界的同时减少了可通行空间的虚假占用，对玻璃环境下的机器人导航具有潜在价值。
这是一篇预印本。论文提出LiDARFlow，将收敛引导向量场与PGFlow面板势流避障相结合，仅用机载Livox LiDAR在未知室内环境中实时生成无碰撞速度指令。在两类真机场景中，无人机成功完成航点导航和方向跟随并避开所有静态障碍，但轨迹存在振荡。该方法计算轻量，可在Raspberry Pi 5上运行，但点云处理是主要瓶颈。
英国研究团队发布预印本，介绍开源移动机械臂 ALFRED。该平台基于商用部件和模块化铝型材框架构建，在一年内完成 528 次森林走廊穿行且未缺席任何计划采集。模型分析显示，机械臂可用工作空间占比从 34.0% 升至 66.1%，仅最终版本 ALFRED 2.0 同时保持前后方向 LiDAR 水平视野无遮挡。设计文件、机器人描述和容器化软件全部开源。
这是一篇预印本，未经同行评审。作者提出 SonarVoxNet，首个在三维声呐数据上输出完整九自由度朝向的潜水员检测器，并发布 Diver3D 数据集。实验显示，将仅偏航的旋转表示升级为连续 6D 全 SO(3) 表示是精度跃升的最大因素，最终 [email protected] 达到 0.774，表明稀疏三维声呐可以恢复潜水员全身朝向。
这是预印本。TACTIC利用多模态大模型结合路边感知状态实时选择并配置物理LiDAR攻击原语，在CARLA仿真中实现100%碰撞率，显著高于固定规则35%和随机60%。结果表明场景相关战术规划能暴露静态攻击遗漏的失效模式，但尚未在真实硬件上验证。
World-action models (WAMs) jointly predict how a scene will evolve and how an agent should act, however joint generation alone does not necessarily impose a shared geometric constraint on these predictions. We present PhysWAM, a unified world-action model for autonomous driving that co-denoises multiview video, metric depth, and ego motion within a single flow-matching transformer.
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。