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.

通过半监督的学习方式，本研究在操作机器人通过感知力及学习示范来识别实物属性并生成所需运动的过程中，采用了力传感、从示范学习、无监督数据和触觉表示编码器等关键技术。结果表明，通过预训练，该学习方法在未知海绵上成功地识别了物理属性并生成了期望的擦拭运动，优于其他无预训练方法。此外，通过实际机器人硬件验证了模型生成的运动，并通过在模拟环境中预训练触觉表示编码器，捕捉到了真实物体的属性并提高了实验结果的泛化能力。