论文标题
一个开放式电信框架，用于生成用于机器人操作中的接触量填充任务的数据
An Open Tele-Impedance Framework to Generate Data for Contact-Rich Tasks in Robotic Manipulation
论文作者
论文摘要
在机器学习中使用大型数据集导致了出色的结果，在某些情况下，在机器上认为不可能的任务中的人数优于人类。但是，在处理物理互动任务时，在接触良好的机器人操作中实现人类水平的表现仍然是一个巨大的挑战。众所周知，规范笛卡尔对此类行动的阻抗对于他们的成功执行至关重要。加强学习（RL）之类的方法可能是解决此类问题的有希望的范式。更确切地说，在解决新任务具有巨大潜力时，使用任务无知的专家演示的方法可以利用大型数据集。但是，现有的数据收集系统是昂贵，复杂的，或者不允许进行阻抗调节。这项工作是朝着数据收集框架迈出的第一步，适合收集与使用新颖的动作空间的RL问题公式相兼容的基于阻抗的专家演示的大型数据集。该框架是根据对机器人操纵的可用数据收集框架进行了广泛分析后根据要求所获得的。结果是一个低成本且开放的远程电信框架，该框架使人类专家能够展示接触式任务。
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.
