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ACME: A Multi-Cultural, Multi-Embodiment Social-Navigation Dataset

ACME is a large-scale, multi-modal dataset for social navigation, collected across 8 sites in 5 countries using 7 robot embodiments. It provides 29.35 hours of onboard data and 43.5 hours of overhead pedestrian tracking, focusing on goal-driven navigation with robot speech interaction in diverse cultural settings.

SourcearXiv RoboticsAuthor: Shashank Rao Marpally, Allan Wang, Atharva Ghotavadekar, Renato Alexandre Ribeiro, Nhat Le, Pilar Bachiller-Burgos, Pranav Goyal, Subham Agrawal, Yasuhiro Nitta, Howard Ziyu Han, Daeun Song, Masaki Kuribayashi, Kohei Uehara, Xiyue Wang, Yangzhe Kong, Duc M. Nguyen, Amirreza Payandeh, Gerardo P\'erez-Gonz\'alez, Alejandro Torrej\'on-Harto, Jeeho Ahn, Tisha Jain, Andrew Stratton, Elvin Yang, Jorge de Heuvel, Nico Ostermann-Myrau, Sai Anudeep Sajja, Mithilya Raj, Daisuke Sato, Gaston Rouquette, Nikolas Martelaro, Maki Sugimoto, Hironobu Takagi, Chieko Asakawa, Maren Bennewitz, Aaron Steinfeld, Xuesu Xiao, Christoforos Mavrogiannis, Harold Soh

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[Submitted on 24 Jul 2026]

Title:ACME: A Multi-Cultural, Multi-Embodiment Social-Navigation Dataset

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Abstract:Understanding how robots and humans move in shared spaces is essential for designing effective social robot navigation policies and predicting human behavior. However, existing datasets often lack the diversity needed to capture differences in culture, geography, and human-robot interaction-factors that strongly shape appropriate social behavior. To address this gap, we introduce ACME: A Cross-cultural, Multi-Embodiment dataset for social navigation. A large-scale data collection effort across 8 sites in 5 countries, using 7 robot embodiments, ACME is a large and diverse multi-modal dataset aimed at advancing social navigation research, providing 29.35 hours of onboard robot data and 43.5 hours of overhead pedestrian tracking data. Unlike prior datasets, it focuses on capturing goal-driven social navigation behavior in complex social scenarios with explicit robot-crowd interaction through robot speech. To facilitate learning navigation policies and predicting pedestrian trajectories, ACME provides 3D and 2D scene features, odometry, interaction information, and human-annotated pedestrian trajectory labels. We make ACME easy to use by providing both human-readable data for each sensor modality as well as raw binary data. Our qualitative and quantitative analyses show that our dataset captures more challenging scenarios and a broader distribution of pedestrian behavior than previous datasets.

Comments: 24 Pages, 19 Figures, Submitted to IJRR on June 29th 2026

Subjects:

Robotics (cs.RO); Artificial Intelligence (cs.AI)

Cite as: arXiv:2607.21964 [cs.RO]

(or arXiv:2607.21964v1 [cs.RO] for this version)

https://doi.org/10.48550/arXiv.2607.21964

arXiv-issued DOI via DataCite (pending registration)

Submission history

From: Shashank Rao Marpally [view email] [v1] Fri, 24 Jul 2026 04:23:21 UTC (20,309 KB)

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