Lifelong-learning robots need to be able to acquire new skills and plan for new tasks over time. Prior works on planning with skills often make assumptions on the structure of skills and tasks, like subgoal skills, shared skill implementations, or learning task-specific plan skeletons, that limit their application to new and different skills and tasks. By contrast, we propose doing task planning by jointly searching in the space of skills and their parameters with skill effect models learned in simulation. Our approach is flexible about skill parameterizations and task specifications, and we use an iterative training procedure to efficiently generate relevant data to train such models. Experiments demonstrate the ability of our planner to integrate new skills in a lifelong manner, finding new task strategies with lower costs in both train and test tasks. We additionally show that our method can transfer to the real world without further fine-tuning.

翻译：终身学习的机器人需要能够获得新的技能和计划,并随着时间推移,新的任务需要获得新的技能和计划。先前的掌握技能的规划工作往往对技能和任务的结构作出假设,例如次级目标技能、共享技能执行或学习特定任务计划骨架,这些假设将限制其应用于新的和不同的技能和任务。相反,我们提议通过在模拟中学习的技能空间和参数以及技能效应模型共同搜索来做任务规划。我们的方法在技能参数和任务规格方面是灵活的,我们使用迭代培训程序来有效地生成相关数据来培训这些模型。实验表明我们的规划者有能力以终身的方式整合新的技能,找到新的任务战略,同时在培训和测试任务中找到成本较低的新任务战略。我们还表明,我们的方法可以在不进一步微调的情况下转移到现实世界。