Constraint-based control approaches offer a flexible way to specify robotic manipulation tasks and execute them on robots with many degrees of freedom. However, the specification of task constraints and their associated priorities usually requires a human-expert and often leads to tailor-made solutions for specific situations. This paper presents our recent efforts to automatically derive task constraints for a constraint-based robot controller from data and adapt them with respect to previously unseen situations (contexts). We use a programming-by-demonstration approach to generate training data in multiple variations (context changes) of a given task. From this data we learn a probabilistic model that maps context variables to task constraints and their respective soft task priorities. We evaluate our approach with 3 different dual-arm manipulation tasks on an industrial robot and show that it performs better than comparable approaches with respect to reproduction accuracy in previously unseen contexts.

基于约束的控制方法提供了一种灵活的方法来指定机器人操纵任务，并在具有许多自由度的机器人上执行它们。但是，任务约束及其相关优先级的规范通常需要人为专家，并经常导致针对特定情况的量身定制的解决方案。本文介绍了我们最近的工作，即从数据中自动导出基于约束的机器人控制器的任务约束，并针对先前未见的情况（上下文）对它们进行调整。我们使用演示编程方法来生成给定任务的多个变体（上下文更改）中的训练数据。从这些数据中，我们学习了一个概率模型，该模型将上下文变量映射到任务约束及其各自的软任务优先级。