用900小时机器人随机探索数据，验证其低成本高效性。
R900: Understanding the Cost-Effectiveness of Random Exploration from 900 Hours of Robotic Data Collection
- 全自动采集流程，无需人工干预
- 1.28百万帧视频用于自监督预训练，提升模型性能
- 适合对低成本数据收集感兴趣的机器人研究者
数据稀缺是机器人操作中模仿学习的关键瓶颈。本文聚焦于通过随机动作在工作空间内采样位置所产生的随机探索数据与视频序列，探究其作为低成本数据源的潜力。研究分为两个范式：(a) 随机动作，评估其自主启动数据采集策略的可行性；(b) 随机探索视频帧，评估其在自监督学习目标下对参数密集型网络的预训练效果。为减少人工监督，我们开发了全自动化流水线，利用云端微服务实现实时监控下的任务标签、终止与重置。基于此，我们在一个非平凡的双层堆叠任务上开展了大规模研究，分析了807小时随机动作、71小时随机探索视频（共128万帧）及1260次策略评估的统计结果。数据集将公开，并可通过云服务访问机器人环境与自动化流程，以支持未来研究。
原文摘要 · Abstract (English)
Data scarcity presents a key bottleneck for imitation learning in robotic manipulation. In this paper, we focus on random exploration data-actions and video sequences produced autonomously via motions to randomly sampled positions in the workspace-to investigate their potential as a cost-effective data source. Our investigation follows two paradigms: (a) random actions, where we assess their feasibility for autonomously bootstrapping data collection policies, and (b) random exploration video frames, where we evaluate their effectiveness in pre-training parameter-dense networks with self-supervised learning objectives. To minimize human supervision, we first develop a fully automated pipeline that handles episode labeling, termination, and resetting using cloud-based microservices for real-time monitoring. Building on this, we present a large-scale study on the cost-effectiveness of real-world random exploration in a non-trivial two-layer stacking task, drawing on statistical results from 807 hours of random actions, 71 hours of random exploration video (1.28M frames), and 1,260 times of policy evaluation. The dataset will be made publicly available and access to the robot environment with the automated pipeline is to be made accessible via cloud service for future research.
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