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.

翻译：在机器学习中利用大规模数据集已取得显著成果，在某些情况下甚至超越了人类在曾被认为机器无法胜任的任务中的表现。然而，在处理物理交互任务（例如高接触机器人操作）时，实现人类级别的性能仍是一大挑战。众所周知，调节笛卡尔阻抗对此类操作的成功执行至关重要。强化学习等方法有望成为解决此类问题的有效范式。更准确地说，利用与任务无关的专家演示来引导学习新任务的方法具有巨大潜力，因为它们可以充分利用大规模数据集。然而，现有数据收集系统成本高昂、结构复杂，或无法实现阻抗调节。本文工作旨在迈向一种适用于收集大规模基于阻抗的专家演示数据集的数据收集框架，该框架与强化学习问题公式化（其中采用新型动作空间）兼容。该框架基于对现有机器人操作数据收集框架的广泛分析所得需求进行设计。最终成果是一个低成本、开放获取的遥阻抗框架，使人类专家能够演示高接触任务。