RoboCade: Gamifying Robot Data Collection
- URL: http://arxiv.org/abs/2512.21235v2
- Date: Fri, 26 Dec 2025 15:54:13 GMT
- Title: RoboCade: Gamifying Robot Data Collection
- Abstract summary: Imitation learning from human demonstrations has become a dominant approach for training autonomous robot policies.<n>We aim to address this scalability challenge by involving a broader audience in a gamified data collection experience.<n>Specifically, we develop a gamified remote teleoperation platform, RoboCade, to engage general users in collecting data.
- Score: 24.3499633171607
- License: http://creativecommons.org/licenses/by/4.0/
- 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.
Related papers
- 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) - What Matters in Learning from Large-Scale Datasets for Robot Manipulation [12.703188997313223]
We conduct a large-scale dataset composition study to answer this question.<n>We develop a data generation framework to procedurally emulate common sources of diversity in existing datasets.<n>We find that camera poses and spatial arrangements are crucial dimensions for both diversity in collection and alignment in retrieval.
arXiv Detail & Related papers (2025-06-16T14:25:29Z) - VITAL: Interactive Few-Shot Imitation Learning via Visual Human-in-the-Loop Corrections [10.49712834719005]
Imitation Learning (IL) has emerged as a powerful approach in robotics, allowing robots to acquire new skills by mimicking human actions.<n>Despite its potential, the data collection process for IL remains a significant challenge due to logistical difficulties and high costs associated with obtaining high-quality demonstrations.<n>We propose a large-scale data generation from a handful of demonstrations through data augmentation in simulation.
arXiv Detail & Related papers (2024-07-30T23:29:47Z) - AdaDemo: Data-Efficient Demonstration Expansion for Generalist Robotic Agent [75.91274222142079]
In this study, we aim to scale up demonstrations in a data-efficient way to facilitate the learning of generalist robotic agents.
AdaDemo is a framework designed to improve multi-task policy learning by actively and continually expanding the demonstration dataset.
arXiv Detail & Related papers (2024-04-11T01:59:29Z) - Polybot: Training One Policy Across Robots While Embracing Variability [70.74462430582163]
We propose a set of key design decisions to train a single policy for deployment on multiple robotic platforms.
Our framework first aligns the observation and action spaces of our policy across embodiments via utilizing wrist cameras.
We evaluate our method on a dataset collected over 60 hours spanning 6 tasks and 3 robots with varying joint configurations and sizes.
arXiv Detail & Related papers (2023-07-07T17:21:16Z) - RH20T: A Comprehensive Robotic Dataset for Learning Diverse Skills in
One-Shot [56.130215236125224]
A key challenge in robotic manipulation in open domains is how to acquire diverse and generalizable skills for robots.
Recent research in one-shot imitation learning has shown promise in transferring trained policies to new tasks based on demonstrations.
This paper aims to unlock the potential for an agent to generalize to hundreds of real-world skills with multi-modal perception.
arXiv Detail & Related papers (2023-07-02T15:33:31Z) - Scaling Robot Learning with Semantically Imagined Experience [21.361979238427722]
Recent advances in robot learning have shown promise in enabling robots to perform manipulation tasks.
One of the key contributing factors to this progress is the scale of robot data used to train the models.
We propose an alternative route and leverage text-to-image foundation models widely used in computer vision and natural language processing.
arXiv Detail & Related papers (2023-02-22T18:47:51Z) - BC-Z: Zero-Shot Task Generalization with Robotic Imitation Learning [108.41464483878683]
We study the problem of enabling a vision-based robotic manipulation system to generalize to novel tasks.
We develop an interactive and flexible imitation learning system that can learn from both demonstrations and interventions.
When scaling data collection on a real robot to more than 100 distinct tasks, we find that this system can perform 24 unseen manipulation tasks with an average success rate of 44%.
arXiv Detail & Related papers (2022-02-04T07:30:48Z) - Actionable Models: Unsupervised Offline Reinforcement Learning of
Robotic Skills [93.12417203541948]
We propose the objective of learning a functional understanding of the environment by learning to reach any goal state in a given dataset.
We find that our method can operate on high-dimensional camera images and learn a variety of skills on real robots that generalize to previously unseen scenes and objects.
arXiv Detail & Related papers (2021-04-15T20:10:11Z) - Visual Imitation Made Easy [102.36509665008732]
We present an alternate interface for imitation that simplifies the data collection process while allowing for easy transfer to robots.
We use commercially available reacher-grabber assistive tools both as a data collection device and as the robot's end-effector.
We experimentally evaluate on two challenging tasks: non-prehensile pushing and prehensile stacking, with 1000 diverse demonstrations for each task.
arXiv Detail & Related papers (2020-08-11T17:58:50Z)
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.