RoboCade: Gamifying Robot Data Collection

2025-12-24

AI Summary

Robotics demonstration data collection is costly and time-consuming, severely limiting the scalability of policy training. To address this, we propose RoboCade—a gamified, web-based teleoperation platform designed for broad accessibility. RoboCade is the first system to systematically integrate game design principles into robotic data collection, introducing the “task-structure overlap” principle to ensure semantic alignment between game tasks and real-world robotic operations. The platform incorporates multimodal feedback—including visual cues, audio effects, progress indicators, achievement badges, and leaderboards—to enhance user engagement. Furthermore, it unifies task abstraction modeling with imitation learning in a joint training framework. Experiments demonstrate that data collected via RoboCade improves success rates across three representative manipulation tasks by 16–56%. A user study confirms a 24% increase in novice user enjoyment, validating the efficacy of gamification in lowering data collection barriers and improving demonstration quality.

Technology Category

• Intelligent Robots: Learning & Optimization for ROB
• Humans and AI: Game Design — Virtual Humans, NPCs and Autonomous Characters
• Machine Learning: Imitation Learning & Inverse Reinforcement Learning

Application Category

• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• User Modeling, Personalization and Recommendation: Metrics for user behavior and evaluating success

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, leaderboards, and badges. We additionally propose principles for constructing gamified tasks that have overlapping structure with useful downstream target tasks. We instantiate RoboCade on three manipulation tasks -- including spatial arrangement, scanning, and insertion. To illustrate the viability of gamified robot data collection, we collect a demonstration dataset through our platform, and show that co-training robot policies with this data can improve success rate on non-gamified target tasks (+16-56%). Further, we conduct a user study to validate that novice users find the gamified platform significantly more enjoyable than a standard non-gamified platform (+24%). These results highlight the promise of gamified data collection as a scalable, accessible, and engaging method for collecting demonstration data.

Problem

• Address scalability of robot demonstration data collection.
• Gamify teleoperation to engage users in data gathering.
• Improve robot policy training with gamified user contributions.

Innovation

• Gamified remote teleoperation platform for data collection
• Embedding gamification strategies in interface and tasks
• Co-training policies with gamified data improves success rates