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

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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.