Bi3: A Biplatform, Bicultural, Biperson Dataset for Social Robot Navigation
- URL: http://arxiv.org/abs/2605.06863v1
- Date: Thu, 07 May 2026 19:05:37 GMT
- Title: Bi3: A Biplatform, Bicultural, Biperson Dataset for Social Robot Navigation
- Abstract summary: 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.
- Score: 5.744787254146161
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: We contribute Bi3, a dataset of social robot navigation among groups of people in a constrained lab space. Compared to prior data collection efforts for social robot navigation, our dataset is unique in that it features: an original experiment design giving rise to close navigation encounters between two humans and a robot; five different navigation algorithms; two different robot platforms; a diverse participant pool of 74 people recruited from two sites in the USA and France; multimodal data streams including 10.5 hours of human and robot ground-truth motion tracks, RGB video, and user impressions over robot performance. Our analysis of the collected dataset through metrics like interaction density and human velocity suggests that Bi3 represents a benchmark of unique diversity and modeling complexity. Bi3 contributes towards understanding how humans and robots can productively mesh their activities in constrained environments, and can be a resource for training models of human motion prediction and robot control policies for navigation in densely crowded spaces.
Related papers
- ACME: A Multi-Cultural, Multi-Embodiment Social-Navigation Dataset [51.60529216343344]
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.
arXiv Detail & Related papers (2026-07-24T04:23:21Z) - 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) - How Human Motion Prediction Quality Shapes Social Robot Navigation Performance in Constrained Spaces [5.744787254146161]
We focus on robot navigation in dynamic and spatially constrained environments.<n>We investigate the effects of human motion prediction quality on robot navigation performance.<n>Key findings include evidence that the widely adopted average displacement error is not a reliable predictor of robot navigation performance.
arXiv Detail & Related papers (2026-01-14T20:34:34Z) - Dexterity from Smart Lenses: Multi-Fingered Robot Manipulation with In-the-Wild Human Demonstrations [52.29884993824894]
Learning multi-fingered robot policies from humans performing daily tasks in natural environments has long been a grand goal in the robotics community.<n>AINA enables learning multi-fingered policies from data collected by anyone, anywhere, and in any environment using Aria Gen 2 glasses.
arXiv Detail & Related papers (2025-11-20T18:59:02Z) - Habitat 3.0: A Co-Habitat for Humans, Avatars and Robots [119.55240471433302]
Habitat 3.0 is a simulation platform for studying collaborative human-robot tasks in home environments.
It addresses challenges in modeling complex deformable bodies and diversity in appearance and motion.
Human-in-the-loop infrastructure enables real human interaction with simulated robots via mouse/keyboard or a VR interface.
arXiv Detail & Related papers (2023-10-19T17:29:17Z) - GNM: A General Navigation Model to Drive Any Robot [67.40225397212717]
General goal-conditioned model for vision-based navigation can be trained on data obtained from many distinct but structurally similar robots.
We analyze the necessary design decisions for effective data sharing across robots.
We deploy the trained GNM on a range of new robots, including an under quadrotor.
arXiv Detail & Related papers (2022-10-07T07:26:41Z) - 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) - Group-Aware Robot Navigation in Crowded Environments [8.154698016722815]
This paper explores learning group-aware navigation policies based on dynamic group formation using deep reinforcement learning.
We show that group-aware policies, compared to baseline policies that neglect human groups, achieve greater robot navigation performance.
Our results contribute to the development of social navigation and the integration of mobile robots into human environments.
arXiv Detail & Related papers (2020-12-22T19:04:40Z)
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.