DABI：在基于双边控制的图像模仿学习中使用下采样的数据增强方法评估
DABI: Evaluation of Data Augmentation Methods Using Downsampling in Bilateral Control-Based Imitation Learning with Images
AI总结：
针对模仿学习中专家数据收集难、传感器频率需匹配的问题，提出DABI数据增强方法，通过高低频数据下采样实现十倍数据扩增，仅用5组演示数据训练Bi-ACT模型，真实实验显著提升成功率。
摘要：
自主机器人操作是一个复杂且持续发展的机器人学领域。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：从专家处收集数据、学习模型、执行。然而，收集专家数据需要人工投入且耗时。此外，由于传感器的数据采集间隔不同，需要进行下采样等预处理以匹配最低频率。下采样既能实现数据增强，也有助于机器人操作的稳定性。基于这一背景，本文提出了面向基于双边控制的图像模仿学习的数据增强方法，名为“DABI”。DABI以1000Hz的频率采集机器人关节角度、速度和力矩，并以100Hz频率采集的夹爪相机和环境相机图像作为数据增强的基础，这使得数据量提升了十倍。本文仅收集了5个专家演示数据集。我们使用未改动的数据集和两种增强方法训练双边控制Bi-ACT模型进行对比实验，并开展了真实世界实验。结果证实成功率有显著提升，从而证明了DABI的有效性。更多材料请查看https://mertcookimg.github.io/dabi。
英文摘要：
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
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
