In many contact-rich tasks, force sensing plays an essential role in adapting the motion to the physical properties of the manipulated object. To enable robots to capture the underlying distribution of object properties necessary for generalising learnt manipulation tasks to unseen objects, existing Learning from Demonstration (LfD) approaches require a large number of costly human demonstrations. Our proposed semi-supervised LfD approach decouples the learnt model into an haptic representation encoder and a motion generation decoder. This enables us to pre-train the first using large amount of unsupervised data, easily accessible, while using few-shot LfD to train the second, leveraging the benefits of learning skills from humans. We validate the approach on the wiping task using sponges with different stiffness and surface friction. Our results demonstrate that pre-training significantly improves the ability of the LfD model to recognise physical properties and generate desired wiping motions for unseen sponges, outperforming the LfD method without pre-training. We validate the motion generated by our semi-supervised LfD model on the physical robot hardware using the KUKA iiwa robot arm. We also validate that the haptic representation encoder, pre-trained in simulation, captures the properties of real objects, explaining its contribution to improving the generalisation of the downstream task.

翻译：在众多接触密集任务中，力觉传感在根据操作对象的物理属性调整运动方式方面发挥着关键作用。为使机器人能够捕捉物体属性的潜在分布，从而将学到的操作任务泛化到未见物体，现有的学习从演示（LfD）方法需要大量昂贵的人类演示。我们提出的半监督LfD方法将学习模型分解为触觉表征编码器和运动生成解码器。这使得我们能够利用大量易获取的无监督数据预训练前者，同时通过少样本LfD训练后者，从而充分发挥从人类学习技能的优势。我们在使用不同刚度和表面摩擦力的海绵进行擦拭任务时验证了该方法。结果表明，预训练显著提升了LfD模型识别物理属性并为未见海绵生成期望擦拭运动的能力，性能优于未预训练的LfD方法。我们使用KUKA iiwa机器人臂在物理机器人硬件上验证了半监督LfD模型生成的运动。同时验证了在仿真环境中预训练的触觉表征编码器能够捕获真实物体的属性，这解释了其提升下游任务泛化能力的贡献。