Learning Object Orientation Constraints and Guiding Constraints for Narrow Passages from One Demonstration

Narrow passages and orientation constraints are very common in manipulation tasks and sampling-based planning methods can be quite time-consuming in such scenarios. We propose a method that can learn object orientation constraints and guiding constraints, represented as Task Space Regions, from a single human demonstrations by analyzing the geometry around the demonstrated trajectory. The key idea of our method is to explore the area around the demonstration trajectory through sampling in task space, and to learn constraints by segmenting and analyzing the feasible samples. Our method is tested on a tire-changing scenario which includes four sub-tasks and on a cupretrieving task. Our results show that our method can produce plans for all these tasks in less than 3 minutes with 50/50 successful trials for all tasks, while baseline methods only succeed 1 out of 50 times in 30 minutes for one of the tasks. The results also show that our method can perform similar tasks with additional obstacles, transfer to similar tasks with different start and/or goal poses, and be used for real-world tasks with a PR2 robot.