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Robots deployed in many real-world settings need to be able to acquire newskills and solve new tasks over time. Prior works on planning with skills oftenmake assumptions on the structure of skills and tasks, such as subgoal skills,shared skill implementations, or task-specific plan skeletons, which limitadaptation to new skills and tasks. By contrast, we propose doing task planningby jointly searching in the space of parameterized skills using high-levelskill effect models learned in simulation. We use an iterative trainingprocedure to efficiently generate relevant data to train such models. Ourapproach allows flexible skill parameterizations and task specifications tofacilitate lifelong learning in general-purpose domains. Experimentsdemonstrate the ability of our planner to integrate new skills in a lifelongmanner, finding new task strategies with lower costs in both train and testtasks. We additionally show that our method can transfer to the real worldwithout further fine-tuning.

Search-Based Task Planning with Learned Skill Effect Models for Lifelong Robotic Manipulation