Learning Skills to Patch Plans Based on Inaccurate Models
Alex Lagrassa,Steven Lee,Oliver Kroemer,Alex Lagrassa,Steven Lee,Oliver Kroemer
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...
