実演からタスクの優先順位を学習する手法
Learning Task Priorities from Demonstrations
TP-GMMをヤコビアンと零空間構造に拡張し、ヒューマノイドの両腕作業など複数タスクが競合する状況での優先順位を実演から学習できるようにした。
著者: João Silvério, Sylvain Calinon, Leonel Rozo, Darwin G. Caldwell
分類: cs.RO
原文アブストラクト
Bimanual operations in humanoids offer the possibility to carry out more than one manipulation task at the same time, which in turn introduces the problem of task prioritization. We address this problem from a learning from demonstration perspective, by extending the Task-Parameterized Gaussian Mixture Model (TP-GMM) to Jacobian and null space structures. The proposed approach is tested on bimanual skills but can be applied in any scenario where the prioritization between potentially conflicting tasks needs to be learned. We evaluate the proposed framework in: two different tasks with humanoids requiring the learning of priorities and a loco-manipulation scenario, showing that the approach can be exploited to learn the prioritization of multiple tasks in parallel.
