Learning Task Priorities from Demonstrations
This addresses task prioritization for humanoid robots in bimanual and loco-manipulation scenarios, but it is incremental as it builds on existing TP-GMM methods.
The paper tackled the problem of learning task priorities for bimanual operations in humanoids by extending the Task-Parameterized Gaussian Mixture Model to Jacobian and null space structures, and demonstrated its effectiveness in tasks requiring prioritization and a loco-manipulation scenario.
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.