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：机器人移动操作因需解决开门、抓取放置物体等多样化任务而极具挑战性。通常，机器人可提供基于第一性原理的基础系统描述，这促使研究者采用基于模型的控制器。然而，机器人动力学及其与物体的交互存在不确定性，限制了控制器的性能。针对这一问题，我们提出一种贝叶斯多任务学习模型，该模型利用三角函数基函数识别动力学中的误差。通过该方法，不同但相关任务的数据可被整合，构建出描述性误差模型，并能在线上高效更新以适配新的未见任务。我们将该学习方案与模型预测控制器相结合，并通过与现有基线控制器的对比实验，充分验证了所提方法的有效性。我们展示了基于球平衡机器人的仿真测试，以及采用四足操作机械臂进行开门操作的硬件实验。