arXiv 2209.104862022-09cs.RO

一种用于机器人操作中接触丰富任务数据生成的开放式遥阻抗框架

An Open Tele-Impedance Framework to Generate Data for Contact-Rich Tasks in Robotic Manipulation

Istituto Italiano di Tecnologia（意大利技术研究院）

Alberto Giammarino, Juan M. Gandarias, Arash Ajoudani

备注：: 6 pages, 4 figures, submitted to IEEE International Conference on Advanced Robotics and Its Social Impacts 2023 (ARSO)

摘要：

在机器学习中使用大型数据集已经取得了出色的结果，在某些情况下，在被认为是机器不可能完成的任务中，它的表现超过了人类。然而，在处理物理交互任务时，例如，在接触丰富的机器人操作中，仍然是一个很大的挑战。众所周知，调节这种操作的笛卡尔阻抗对于它们的成功执行是至关重要的。像强化学习（RL）这样的方法可能是解决此类问题的一个很有前途的范例。更确切地说，在解决新任务时使用任务不可知的专家演示来引导学习的方法具有巨大的潜力，因为它们可以利用大型数据集。然而，现有的数据汇集系统昂贵、复杂，或者不允许阻抗调节。这项工作是迈向数据汇集框架的第一步，该框架适用于收集与RL问题制定兼容的基于阻抗的专家演示的大型数据集，其中使用了一种新的动作空间。该框架是根据广泛分析机器人操作的可用数据汇集框架后获得的要求设计的。其结果是一个低成本和开放访问的远程阻抗框架，使人类专家能够演示接触丰富的任务。

英文摘要：

Using large datasets in machine learning has led to outstanding results, in some cases outperforming humans in tasks that were believed impossible for machines. However, achieving human-level performance when dealing with physically interactive tasks, e.g., in contact-rich robotic manipulation, is still a big challenge. It is well known that regulating the Cartesian impedance for such operations is of utmost importance for their successful execution. Approaches like reinforcement Learning (RL) can be a promising paradigm for solving such problems. More precisely, approaches that use task-agnostic expert demonstrations to bootstrap learning when solving new tasks have a huge potential since they can exploit large datasets. However, existing data collection systems are expensive, complex, or do not allow for impedance regulation. This work represents a first step towards a data collection framework suitable for collecting large datasets of impedance-based expert demonstrations compatible with the RL problem formulation, where a novel action space is used. The framework is designed according to requirements acquired after an extensive analysis of available data collection frameworks for robotics manipulation. The result is a low-cost and open-access tele-impedance framework which makes human experts capable of demonstrating contact-rich tasks.

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。

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