在人类共享的户外空间运行的轮腿式机器人需要准确的感知能力、符合社会规范的导航能力、未来场景推理能力以及场景重建能力。然而，现有数据集通常仅孤立地支持这些能力，且很少在真实的轮腿式平台上提供密集的空间和行为级监督。我们提出了 LynxScenes，这是一个使用 LYNX M20 轮腿式机器人采集的大规模真实世界数据集和基准。该数据集包含约 10 万个时长为 30 秒的导航片段，总时长达 833.6 小时，覆盖里程 3,294.7 公里，并包含 300 万帧标注数据。同步记录的数据包括四个鱼眼相机、双激光雷达（LiDAR）、惯性测量单元（IMU）、里程计和机器人状态。标注内容包括涵盖 27 个类别的 2082 万个有向 3D 边界框、1389 万个道路结构标注，以及分为三层并与连续未来轨迹对齐的 20 条导航指令。LynxScenes 支持四项互补任务：基于指令的轨迹预测、基于鸟瞰图（BEV）的 3D 物体和道路结构感知、未来场景预测以及多视图场景重建。针对轨迹预测，我们进一步引入了一种基于日志重放的伪闭环评估器，用于衡量超越轨迹模仿的安全性、进度和指令合规性。对代表性导航、感知、世界模型和重建方法的广泛评估表明，近距离户外机器人导航仍面临重大挑战。该数据集和评估工具包计划公开发布。

Wheel-legged robots operating in outdoor spaces shared with humans require accurate perception, socially compliant navigation, future-scene reasoning, and scene reconstruction. However, existing datasets typically support these capabilities in isolation and rarely provide dense spatial and behavior-level supervision on real wheel-legged platforms. We introduce LynxScenes, a large-scale real-world dataset and benchmark collected with a LYNX M20 wheel-legged robot. It contains approximately 100K 30-s navigation clips spanning 833.6 h and 3,294.7 km, with 3.0M annotated frames. The synchronized recordings include four fisheye cameras, dual LiDARs, IMU, odometry, and robot states. Annotations comprise 20.82M oriented 3D boxes across 27 categories, 13.89M road-structure annotations, and 20 navigation instructions organized into three layers and aligned with continuous future trajectories. LynxScenes supports four complementary tasks: instruction-conditioned trajectory prediction, BEV-based perception of 3D objects and road structures, future scene prediction, and multi-view scene reconstruction. For trajectory prediction, we further introduce a log-replay-based pseudo-closed-loop evaluator that measures safety, progress, and instruction compliance beyond trajectory imitation. Extensive evaluations across representative navigation, perception, world-model, and reconstruction methods reveal substantial remaining challenges in close-range outdoor robot navigation. The dataset and evaluation toolkit are planned for public release.