HABIT: Human-Aware Behavior and Interaction Training Dataset for Robot Manipulation
Overview
This research presents HABIT, a large-scale demonstration dataset for robot manipulation in human-present environments. The dataset includes over 10,000 episodes spanning more than 160 hours across 60 tasks, organized into three interaction roles: Collaborator (joint task completion), Coworker (separate tasks in shared space), and Supervisor (human directing the robot). Experiments indicate that training on this data enables robots to exhibit human-aware behaviors such as spatiotemporal synchronization, yielding, and gesture grounding, which are not produced by models trained solely on human-absent data. The work aims to extend robot policies to environments shared with humans by treating human presence as a new axis of dataset diversity.
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Reported quantity · episodes: 10000 other · reported trials
“dataset for human-present environments. We organize tasks into three roles capturing distinct modes of human-robot interaction: Collaborator, where human and robot jointly accomplish a task; Coworker, where they pursue separate tasks in a shared space; and Supervisor, where the human directs the robot. The dataset comprises over 10K episodes and over 160 hours across 60 tasks. Our experiments show that training on h”
Exact source · revision 1Source owner not reported
Reported quantity · Reported duration: 160 hours · Basis not reported
“dataset for human-present environments. We organize tasks into three roles capturing distinct modes of human-robot interaction: Collaborator, where human and robot jointly accomplish a task; Coworker, where they pursue separate tasks in a shared space; and Supervisor, where the human directs the robot. The dataset comprises over 10K episodes and over 160 hours across 60 tasks. Our experiments show that training on h”
Exact source · revision 1Source owner not reported
Reported quantity · tasks: 60 other · Basis not reported
“dataset for human-present environments. We organize tasks into three roles capturing distinct modes of human-robot interaction: Collaborator, where human and robot jointly accomplish a task; Coworker, where they pursue separate tasks in a shared space; and Supervisor, where the human directs the robot. The dataset comprises over 10K episodes and over 160 hours across 60 tasks. Our experiments show that training on h”
Exact source · revision 1Source owner not reported
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- arXiv Robotics — research abstractsSignalHABIT: Human-Aware Behavior and Interaction Training Dataset for Robot Manipulation
This research presents HABIT, a large-scale demonstration dataset for robot manipulation in human-present environments. The dataset includes over 10,000 episodes spanning more than 160 hours across 60 tasks, organized into three interaction roles: Collaborator (joint task completion), Coworker (separate tasks in shared space), and Supervisor (human directing the robot). Experiments indicate that training on this data enables robots to exhibit human-aware behaviors such as spatiotemporal synchronization, yielding, and gesture grounding, which are not produced by models trained solely on human-absent data. The work aims to extend robot policies to environments shared with humans by treating human presence as a new axis of dataset diversity.
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