The combination of behavioural cloning and neural networks has driven significant progress in robotic manipulation. As these algorithms may require a large number of demonstrations for each task of interest, they remain fundamentally inefficient in complex scenarios. This issue is aggravated when the system is treated as a black-box, ignoring its physical properties. This work characterises widespread properties of robotic manipulation, such as pose equivariance and locality. We empirically demonstrate that transformations arising from each of these properties allow neural policies trained with behavioural cloning to better generalise to out-of-distribution problem instances.

翻译：行为克隆与神经网络的结合推动了机器人操作领域的显著进展。由于这些算法可能需要对每个感兴趣的任务进行大量演示，因此在复杂场景中它们本质上仍然效率低下。当系统被当作黑箱处理、忽略其物理特性时，这一问题会进一步加剧。本研究刻画了机器人操作中普遍存在的特性，如姿态等变性与局部性。我们通过实验证明，由这些特性产生的变换能使通过行为克隆训练的神经策略更好地泛化到分布外的问题实例。