Mobile manipulation in robotics is challenging due to the need of solving many diverse tasks, such as opening a door or picking-and-placing an object. Typically, a basic first-principles system description of the robot is available, thus motivating the use of model-based controllers. However, the robot dynamics and its interaction with an object are affected by uncertainty, limiting the controller's performance. To tackle this problem, we propose a Bayesian multi-task learning model that uses trigonometric basis functions to identify the error in the dynamics. In this way, data from different but related tasks can be leveraged to provide a descriptive error model that can be efficiently updated online for new, unseen tasks. We combine this learning scheme with a model predictive controller, and extensively test the effectiveness of the proposed approach, including comparisons with available baseline controllers. We present simulation tests with a ball-balancing robot, and door-opening hardware experiments with a quadrupedal manipulator.
翻译:机器人移动操作是一个具有挑战性的任务,因为它需要解决许多不同的任务,比如打开门或拾取物品。通常,机器人的基本系统描述可用,因此使用基于模型的控制器的方法是合理的。然而,机器人动力学及其与物体的交互受到不确定性的影响,这限制了控制器的性能。为了解决这个问题,我们提出了一个贝叶斯多任务学习模型,该模型使用三角基函数来识别动力学误差。通过这种方式,不同但相关的任务数据可以被利用来提供一个描述性误差模型,该模型可以被有效地在线更新到新的未见过的任务中。我们将这个学习方案与模型预测控制器相结合,并广泛测试所提出方法的有效性,包括与可用的基准控制器的比较。我们使用一个平衡球机器人进行了模拟测试,并使用四足机器人做了开门实验。