通过降采样实现图像驱动的模仿学习数据增强，提升机器人操作成功率。
DABI: Evaluation of Data Augmentation Methods Using Downsampling in Bilateral Control-Based Imitation Learning with Images
- 以100Hz图像与1000Hz运动数据结合，降采样生成十倍增数据
- 仅用5组专家示范数据，成功率显著提升
- 适合关注机器人模仿学习数据效率的研究者
自主机器人操作是复杂且不断发展的机器人领域。本文聚焦于模仿学习中的数据增强方法。模仿学习包含三个阶段：从专家收集数据、学习模型、执行。然而，专家数据采集需人工投入且耗时。此外，由于传感器采样频率不同，需进行降采样以匹配最低频率，这不仅必要，还可用于数据增强并稳定机器人操作。基于此背景，本文提出一种基于双边控制的图像模仿学习数据增强方法——DABI。DABI以1000 Hz采集机器人关节角度、速度和力矩，以100 Hz的夹持器和环境相机图像作为基础进行数据增强，实现数据量十倍增长。本文仅使用5组专家示范数据，训练双边控制的Bi-ACT模型，并对比原始数据集及两种增强方法，开展真实世界实验。结果表明成功率显著提高，验证了DABI的有效性。
原文摘要 · Abstract (English)
Autonomous robot manipulation is a complex and continuously evolving robotics field. This paper focuses on data augmentation methods in imitation learning. Imitation learning consists of three stages: data collection from experts, learning model, and execution. However, collecting expert data requires manual effort and is time-consuming. Additionally, as sensors have different data acquisition intervals, preprocessing such as downsampling to match the lowest frequency is necessary. Downsampling enables data augmentation and also contributes to the stabilization of robot operations. In light of this background, this paper proposes the Data Augmentation Method for Bilateral Control-Based Imitation Learning with Images, called "DABI". DABI collects robot joint angles, velocities, and torques at 1000 Hz, and uses images from gripper and environmental cameras captured at 100 Hz as the basis for data augmentation. This enables a tenfold increase in data. In this paper, we collected just 5 expert demonstration datasets. We trained the bilateral control Bi-ACT model with the unaltered dataset and two augmentation methods for comparative experiments and conducted real-world experiments. The results confirmed a significant improvement in success rates, thereby proving the effectiveness of DABI. For additional material, please check https://mertcookimg.github.io/dabi
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