We address the problem of devising the means for a robot to rapidly and safely learn insertion skills with just a few human interventions and without hand-crafted rewards or demonstrations. Our InsertionNet version 2.0 provides an improved technique to robustly cope with a wide range of use-cases featuring different shapes, colors, initial poses, etc. In particular, we present a regression-based method based on multimodal input from stereo perception and force, augmented with contrastive learning for the efficient learning of valuable features. In addition, we introduce a one-shot learning technique for insertion, which relies on a relation network scheme to better exploit the collected data and to support multi-step insertion tasks. Our method improves on the results obtained with the original InsertionNet, achieving an almost perfect score (above 97.5$\%$ on 200 trials) in 16 real-life insertion tasks while minimizing the execution time and contact during insertion. We further demonstrate our method's ability to tackle a real-life 3-step insertion task and perfectly solve an unseen insertion task without learning.

解决机器人快速、安全地学习插入技能的问题。提出了一种基于InsertionNet的方法，利用多模态输入和对比学习实现高效特征学习；引入一种关系网络方案实现了能够支持多步插入任务的一次性学习技术。该方法在16个真实插入任务中实现了近乎完美的得分，并最小化了插入时的执行时间和接触。同时，展示了该方法解决一个真实的三步插入任务并无需学习的能力。

InsertionNet 2.0: 使用多模态多视觉感知输入进行最小接触、多步插入