Human-like continual robot learning based on three-level computational energy cost regulation

在项目的第一年，选择了一个合适的机器人模拟器环境(PYBullet)，并在其上开发了终身机器人学习(LRL)模型的软件平台。对于LRL，考虑了具有三个任务(T1、T2、T3)的机械臂。机器人的动作被建模为以不同的角度打击对象。LRL任务被设置为对自由空间(T1)、变向墙(T2)、L形变向墙(T3)三种不同环境中动作效果的预测。任务的执行基于学习进度(LP)，而“神经成本”的考虑则留到明年。在每个任务的神经网络之间建立了一个基本的知识传递体系。还探索了符号形成组件，但没有将其合并到模拟的LRL模型中。在开发LRL模式的同时，开展了支持工作，出版了几份出版物，并与合作者一起举办了IROS 2022年讲习班。在一项研究中，研究了利用深层神经网络潜在层中的离散单元来形成符号的工作[2]。此外，与合作者一起进行了一项关于机器人信任的工作，该工作使用“神经计算成本”来形成对社会伙伴的信任[3]。因此，LRL模型可以扩展到包括信任形成，即使它不是最初提案的直接组成部分。此外，为了支持与人-机器人相关的任务，还致力于教机器人如何基于人类演示进行纠错。

In the first year of the project an appropriate robot simulator environment is selected (Pybullet) and the software platform for Lifelong Robot Learning (LRL) model has been developed on it. A robotic arm with three tasks (T1,T2,T3) is considered for LRL. The robot action is modeled as hitting objects with different angles. LRL tasks are set as the prediction of the effects of the actions in the three different environments, free space (T1), wall with changing orientation (T2) , L-shaped wall with changing orientation (T3). Task execution is based on Learning Progress (LP) whereas ‘neural cost’ consideration is left for next year. A basic knowledge transfer architecture is developed among the neural networks of each task. The symbol formation component is also explored but not incorporated into the simulated LRL model. Parallel to the development of the LRL model, supporting work is conducted and several publications are produced, and a workshop in IROS 2022 is held together with collaborators. In one line of research, work on symbol formation by the use of discrete units in the latent layers of deep neural networks is studied [2]. In addition, a work on robotic trust is conducted with collaborators which uses ‘neural computational cost’ for forming trust in social partners [3]. Therefore the LRL model can be extended to include trust formation, even though it was not directly part of the initial proposal. In addition, for supporting human-robot related tasks some work is devoted to teaching robots how to correct errors based on human demonstration.