VITAL：通过视觉人在回路修正的交互式少样本模仿学习
VITAL: Interactive Few-Shot Imitation Learning via Visual Human-in-the-Loop Corrections
AI总结：
本文提出VITAL，一种通过模拟数据增强和人在回路修正的少样本模仿学习方法，在多种机器人任务中验证了其有效性和泛化能力。
摘要：
模仿学习（IL）已成为机器人学中的一种强大方法，使机器人能够通过模仿人类动作来获取新技能。尽管其潜力巨大，但由于获取高质量示范的后勤困难和成本高昂，IL的数据收集过程仍然是一个重大挑战。为解决这些问题，我们提出了一种通过模拟中的数据增强，从少量示范中生成大规模数据的方法。我们的方法利用经济实惠的硬件和视觉处理技术来收集示范，然后通过增强生成用于模仿学习的广泛训练数据集。通过利用真实和模拟环境，以及人在回路修正，我们增强了所学策略的泛化性和鲁棒性。我们在模拟和真实机器人环境中进行了多轮实验，评估了我们的方法，重点关注不同复杂度的任务，包括收集瓶子、堆叠物体和敲击锤子。我们的实验结果验证了我们的方法在从模拟数据中学习鲁棒机器人策略方面的有效性，并显著受益于人在回路修正和真实世界数据的整合。此外，我们展示了该框架对新任务（如设置饮料托盘）的泛化能力，展示了其适应性和处理广泛真实世界操作任务的潜力。实验视频可在以下网址观看：https://youtu.be/YeVAMRqRe64?si=R179xDlEGc7nPu8i
英文摘要：
Imitation Learning (IL) has emerged as a powerful approach in robotics, allowing robots to acquire new skills by mimicking human actions. Despite its potential, the data collection process for IL remains a significant challenge due to the logistical difficulties and high costs associated with obtaining high-quality demonstrations. To address these issues, we propose a large-scale data generation from a handful of demonstrations through data augmentation in simulation. Our approach leverages affordable hardware and visual processing techniques to collect demonstrations, which are then augmented to create extensive training datasets for imitation learning. By utilizing both real and simulated environments, along with human-in-the-loop corrections, we enhance the generalizability and robustness of the learned policies. We evaluated our method through several rounds of experiments in both simulated and real-robot settings, focusing on tasks of varying complexity, including bottle collecting, stacking objects, and hammering. Our experimental results validate the effectiveness of our approach in learning robust robot policies from simulated data, significantly improved by human-in-the-loop corrections and real-world data integration. Additionally, we demonstrate the framework's capability to generalize to new tasks, such as setting a drink tray, showcasing its adaptability and potential for handling a wide range of real-world manipulation tasks. A video of the experiments can be found at: https://youtu.be/YeVAMRqRe64?si=R179xDlEGc7nPu8i
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。
