Adaptation capabilities, like damage recovery, are crucial for the deployment
of robots in complex environments. Several works have demonstrated that using
repertoires of pre-trained skills can enable robots to adapt to unforeseen
mechanical damages in a few minutes. These adaptation capabilities are directly
linked to the behavioural diversity in the repertoire. The more alternatives
the robot has to execute a skill, the better are the chances that it can adapt
to a new situation. However, solving complex tasks, like maze navigation,
usually requires multiple different skills. Finding a large behavioural
diversity for these multiple skills often leads to an intractable exponential
growth of the number of required solutions. In this paper, we introduce the
Hierarchical Trial and Error algorithm, which uses a hierarchical behavioural
repertoire to learn diverse skills and leverages them to make the robot more
adaptive to different situations. We show that the hierarchical decomposition
of skills enables the robot to learn more complex behaviours while keeping the
learning of the repertoire tractable. The experiments with a hexapod robot show
that our method solves maze navigation tasks with 20% less actions in the most
challenging scenarios than the best baseline while having 57% less complete
failures.

介绍了分层试错算法，使用分层行为复述学习各种技能并利用这些技能使机器人更适应不同情况。实验表明，与最佳基线相比，我们的方法在迷宫导航任务中需要的行动次数少 20％，而完全失败的通常情况下减少 57％。