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

• URL: http://arxiv.org/abs/2607.21964v1
• Date: Fri, 24 Jul 2026 04:23:21 GMT
• Title: ACME: A Multi-Cultural, Multi-Embodiment Social-Navigation Dataset
• Abstract summary: ACME: A Cross-cultural, Multi-Embodiment dataset for social navigation.<n>ACME is a large-scale data collection effort across 8 sites in 5 countries, using 7 robot embodiments.<n>Unlike prior datasets, it focuses on capturing goal-driven social navigation behavior in complex social scenarios with explicit robot-crowd interaction through robot speech.
• Score: 51.60529216343344
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

Related papers

• Bi3: A Biplatform, Bicultural, Biperson Dataset for Social Robot Navigation [5.744787254146161]
  Bi3 is a dataset of social robot navigation among groups of people in a constrained lab space.<n>Bi3 contributes towards understanding how humans and robots can productively mesh their activities in constrained environments.
  arXiv  Detail & Related papers  (2026-05-07T19:05:37Z)
• EgoVerse: An Egocentric Human Dataset for Robot Learning from Around the World [63.2070298441355]
  EgoVerse is a collaborative platform for human data-driven robot learning.<n>We release 1,362 hours (80k episodes) of human demonstrations spanning 1,965 tasks, 240 scenes, and 2,087 unique demonstrators.<n>We conduct a large-scale study of human-to-robot transfer with experiments replicated across multiple labs, tasks, and robot embodiments.
  arXiv  Detail & Related papers  (2026-04-08T21:27:06Z)
• Teaching Robots Like Dogs: Learning Agile Navigation from Luring, Gesture, and Speech [19.38205427861322]
  We aim to enable legged robots to learn how to interpret human social cues and produce appropriate behaviors through physical human guidance.<n>We propose a human-in-the-loop framework that enables robots to acquire navigational behaviors in a data-efficient manner.<n>We evaluate our framework across six real-world agile navigation scenarios, including jumping over or avoiding obstacles.
  arXiv  Detail & Related papers  (2026-01-13T10:47:12Z)
• HHI-Assist: A Dataset and Benchmark of Human-Human Interaction in Physical Assistance Scenario [63.77482302352545]
  HHI-Assist is a dataset comprising motion capture clips of human-human interactions in assistive tasks.<n>Our work has the potential to significantly enhance robotic assistance policies.
  arXiv  Detail & Related papers  (2025-09-12T09:38:17Z)
• Social-Transmotion: Promptable Human Trajectory Prediction [65.80068316170613]
  Social-Transmotion is a generic Transformer-based model that exploits diverse and numerous visual cues to predict human behavior.<n>Our approach is validated on multiple datasets, including JTA, JRDB, Pedestrians and Cyclists in Road Traffic, and ETH-UCY.
  arXiv  Detail & Related papers  (2023-12-26T18:56:49Z)
• A Study on Learning Social Robot Navigation with Multimodal Perception [6.052803245103173]
  We present a study on learning social robot navigation with multimodal perception using a large-scale real-world dataset. We compare unimodal and multimodal learning approaches against a set of classical navigation approaches in different social scenarios. The results show that multimodal learning has a clear advantage over unimodal learning in both dataset and human studies.
  arXiv  Detail & Related papers  (2023-09-22T01:47:47Z)
• SACSoN: Scalable Autonomous Control for Social Navigation [62.59274275261392]
  We develop methods for training policies for socially unobtrusive navigation. By minimizing this counterfactual perturbation, we can induce robots to behave in ways that do not alter the natural behavior of humans in the shared space. We collect a large dataset where an indoor mobile robot interacts with human bystanders.
  arXiv  Detail & Related papers  (2023-06-02T19:07:52Z)
• BEHAVE: Dataset and Method for Tracking Human Object Interactions [105.77368488612704]
  We present the first full body human- object interaction dataset with multi-view RGBD frames and corresponding 3D SMPL and object fits along with the annotated contacts between them. We use this data to learn a model that can jointly track humans and objects in natural environments with an easy-to-use portable multi-camera setup.
  arXiv  Detail & Related papers  (2022-04-14T13:21:19Z)
• Socially Compliant Navigation Dataset (SCAND): A Large-Scale Dataset of Demonstrations for Social Navigation [92.66286342108934]
  Social navigation is the capability of an autonomous agent, such as a robot, to navigate in a'socially compliant' manner in the presence of other intelligent agents such as humans. Our dataset contains 8.7 hours, 138 trajectories, 25 miles of socially compliant, human teleoperated driving demonstrations.
  arXiv  Detail & Related papers  (2022-03-28T19:09:11Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.