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• W3129364384 abstract "Planners using accurate models can be effective for accomplishing manipulation tasks in the real world, but are typically highly specialized and require significant fine-tuning to be reliable. Meanwhile, learning is useful for adaptation, but can require a substantial amount of data collection. In this paper, we propose a method that improves the efficiency of sub-optimal planners with approximate but simple and fast models by switching to a model-free policy when unexpected transitions are observed. Unlike previous work, our method specifically addresses when the planner fails due to transition model error by patching with a local policy only where needed. First, we use a sub-optimal model-based planner to perform a task until model failure is detected. Next, we learn a local model-free policy from expert demonstrations to complete the task in regions where the model failed. To show the efficacy of our method, we perform experiments with a shape insertion puzzle and compare our results to both pure planning and imitation learning approaches. We then apply our method to a door opening task. Our experiments demonstrate that our patch-enhanced planner performs more reliably than pure planning and with lower overall sample complexity than pure imitation learning." @default.
• W3129364384 created "2021-03-01" @default.
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• W3129364384 date "2020-10-24" @default.
• W3129364384 modified "2023-09-26" @default.
• W3129364384 title "Learning Skills to Patch Plans Based on Inaccurate Models" @default.
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• W3129364384 doi "https://doi.org/10.1109/iros45743.2020.9341475" @default.
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