Amidst the wide popularity of imitation learning algorithms in robotics, their properties regarding hyperparameter sensitivity, ease of training, data efficiency, and performance have not been well-studied in high-precision industry-inspired environments. In this work, we demonstrate the limitations and benefits of prominent imitation learning approaches and analyze their capabilities regarding these properties. We evaluate each algorithm on a complex bimanual manipulation task involving an over-constrained dynamics system in a setting involving multiple contacts between the manipulated object and the environment. While we find that imitation learning is well suited to solve such complex tasks, not all algorithms are equal in terms of handling environmental and hyperparameter perturbations, training requirements, performance, and ease of use. We investigate the empirical influence of these key characteristics by employing a carefully designed experimental procedure and learning environment. Paper website: https://bimanual-imitation.github.io/

翻译：在模仿学习算法于机器人学领域广受欢迎之际，其在超参数敏感性、训练便捷性、数据效率及性能表现等方面的特性，尚未在工业级高精度环境中得到充分研究。本工作揭示了主流模仿学习方法的局限与优势，并针对上述特性系统分析了其能力边界。我们在涉及多接触约束的复杂双手操作任务上评估了各算法性能，该任务包含被操作物体与环境之间产生多重接触的过约束动力学系统。研究发现，虽然模仿学习整体适用于解决此类复杂任务，但不同算法在处理环境扰动与超参数变化、训练需求、性能表现及易用性方面存在显著差异。通过采用精心设计的实验流程与学习环境，我们深入探究了这些关键特性的实证影响。论文网站：https://bimanual-imitation.github.io/