RoboCade: Gamifying Robot Data Collection
Suvir Mirchandani, Mia Tang, Jiafei Duan, Jubayer Ibn Hamid, Michael Cho, Dorsa Sadigh

TL;DR
This paper introduces RoboCade, a gamified platform for remote robot data collection that engages users and improves policy success rates, addressing scalability and accessibility issues in imitation learning.
Contribution
We develop RoboCade, a novel gamified system for robot data collection, demonstrating its effectiveness in improving policy success and user engagement.
Findings
Gamified data collection improves robot policy success rates (+16-56%).
Users find the gamified platform significantly more enjoyable (+24%).
RoboCade enables scalable, accessible robot data collection.
Abstract
Imitation learning from human demonstrations has become a dominant approach for training autonomous robot policies. However, collecting demonstration datasets is costly: it often requires access to robots and needs sustained effort in a tedious, long process. These factors limit the scale of data available for training policies. We aim to address this scalability challenge by involving a broader audience in a gamified data collection experience that is both accessible and motivating. Specifically, we develop a gamified remote teleoperation platform, RoboCade, to engage general users in collecting data that is beneficial for downstream policy training. To do this, we embed gamification strategies into the design of the system interface and data collection tasks. In the system interface, we include components such as visual feedback, sound effects, goal visualizations, progress bars,…
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Taxonomy
TopicsRobot Manipulation and Learning · Social Robot Interaction and HRI · Virtual Reality Applications and Impacts
