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This paper introduces a large real-world dataset and benchmark for training and evaluating learned perception, trajectory prediction, world models, and scene reconstruction on an outdoor wheel-legged robot. Its value to AI readers lies in the scale of the data (about 834 hours and 3.0M annotated frames) and in the pseudo-closed-loop evaluator, which tries to measure safety and instruction compliance beyond imitation accuracy. The authors report substantial remaining challenges, and the dataset and toolkit are only planned for release, so independent validation is still pending.
LynxScenes: A Large-Scale Real-World Benchmark for Outdoor Wheel-Legged Robot Perception, Navigation, Imagination and Reconstruction
arXiv:2610.18093v1 Announce Type: new Abstract: 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.
Abstract: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.
20 pages, 9 figures. Includes appendices
Robotics (cs.RO)
arXiv:2610.18093 [cs.RO]
(or arXiv:2610.18093v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2610.18093 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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