HumanoidVLN: A Physics-Grounded Simulator and Benchmark for Vision-Language Navigation Across Diverse Humanoid Embodiments
HumanoidVLN is a physics-grounded simulator and benchmark for vision-language navigation (VLN) on humanoid robots. Built on NVIDIA Isaac Sim, it supports four humanoid platforms with 10–12 lower-body degrees of freedom and heights from 1.17m to 1.80m. It provides 933 collision-aware episodes with fine-grained instructions and three stylistic variants. JanusVLN achieves the best mean success rate of 43.55% and nDTW of 48.38, and a 20-episode sim-to-real pilot shows strong correlation (r=0.935).
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[Submitted on 13 Aug 2026]
Title:HumanoidVLN: A Physics-Grounded Simulator and Benchmark for Vision-Language Navigation Across Diverse Humanoid Embodiments
View a PDF of the paper titled HumanoidVLN: A Physics-Grounded Simulator and Benchmark for Vision-Language Navigation Across Diverse Humanoid Embodiments, by Quan-Dung Pham and 10 other authors
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Abstract:Vision-Language Navigation (VLN) for humanoid robots poses challenges existing benchmarks fail to address: bipedal locomotion imposes physical constraints absent from wheeled agents, humanoid morphologies vary across platforms, and egocentric observations are distorted by locomotion-induced camera dynamics. We present HumanoidVLN, a physics-grounded simulator and benchmark for VLN across diverse humanoid embodiments. Built on NVIDIA Isaac Sim, our platform supports an extensible set of humanoid configurations, demonstrated on four robots (Unitree G1, Unitree H1, Internal-A, Internal-B) spanning 10-12 lower-body DoF and heights from 1.17m to 1.80m, via a hierarchical control stack combining a reinforcement learning locomotion policy with interchangeable PD or MPC path trackers. New robots and VLN models integrate with minimal effort; we demonstrate compatibility with NaVILA, DualVLN, StreamVLN, and JanusVLN. Environments are drawn from artist-designed scenes and 3D Gaussian Splatting reconstructions, filtered for navigable areas exceeding 100 square meters. Instructions are generated by a dual generator-reviewer plus paraphraser multi-agent pipeline with human-in-the-loop verification, yielding 933 collision-aware reference episodes, each paired with one fine-grained instruction and three coarse-grained stylistic variants (formal, natural, casual). Across four models and four embodiments, JanusVLN achieves the highest mean success rate of 43.55% and nDTW of 48.38. In a 20-episode sim-to-real pilot with DualVLN and the Unitree G1, navigation errors correlate strongly (r=0.935), with a mean absolute difference of 0.68m and mean trajectory similarity of 0.782 (+/-0.188) nDTW. These results highlight the interaction between VLN models, controllers, and humanoid embodiments under physical execution. Code, benchmark, and data will be released upon acceptance at this https URL.
Subjects:
Robotics (cs.RO)
Cite as: arXiv:2608.12860 [cs.RO]
(or arXiv:2608.12860v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2608.12860
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Quan Dung Pham [view email] [v1] Thu, 13 Aug 2026 06:16:05 UTC (2,208 KB)
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