Manipulation Planning with Tight Geometric Constraints

Many everyday tasks have tight geometric constraints that make sampling-based planning difficult or some task actions infeasible. In these cases, the widely used symbolic planner-based methods become inefficient because symbolic planning with sampled instances exploits only heuristics on symbolic search but has no proper heuristic on which instance to sample first. This issue not only wastes time generating useless samples, but it also enlarges symbolic planning problems, which significantly reduce overall planning efficiency as iteration continues.

We created a hierarchical planning algorithm based on a reachability tree. To improve the algorithm's speed even further, the results of low-level planning are used to inform the high-level planner, allowing for better high-level planning based on experience.

Contact : Kanghyun Kim (kh11kim@kaist.ac.kr)

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Related Publications

2023 ICRA "A Reachability Tree-Based Algorithm for Robot Task and Motion Planning" K Kim, D Park, and M J Kim (Accepted) [IEEE] [arXiv]

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