与移动操纵器实际互动任务混合学习和优化框架 (A Hybrid Learning and Optimization Framework to Achieve Physically Interactive Tasks with Mobile Manipulators)

from arxiv, 8 pages, 6 figures, accepted by IEEE Robotics and Automation Letters and IEEE International Conference on Intelligent Robots and Systems 2022

This paper proposes a hybrid learning and optimization framework for mobile manipulators for complex and physically interactive tasks. The framework exploits an admittance-type physical interface to obtain intuitive and simplified human demonstrations and Gaussian Mixture Model (GMM)/Gaussian Mixture Regression (GMR) to encode and generate the learned task requirements in terms of position, velocity, and force profiles. Next, using the desired trajectories and force profiles generated by GMM/GMR, the impedance parameters of a Cartesian impedance controller are optimized online through a Quadratic Program augmented with an energy tank to ensure the passivity of the controlled system. Two experiments are conducted to validate the framework, comparing our method with two approaches with constant stiffness (high and low). The results showed that the proposed method outperforms the other two cases in terms of trajectory tracking and generated interaction forces, even in the presence of disturbances such as unexpected end-effector collisions.

翻译：本文为移动操纵器的复杂和物理互动任务提出了一个混合学习和优化框架。框架利用一种入门型物理界面,获得直观和简化的人类演示,利用高森混血体模型(GMM)/高森混血体回归模型(GMR)对位置、速度和力量剖面进行编码和生成学习的任务要求。接着,利用GMM/GMR生成的预期轨迹和力量剖面,在网上优化了卡泰因阻力控制器的阻力参数,通过一个配有能源罐的横幅程序,确保受控系统的被动性。进行了两次试验,以验证框架,将我们的方法与两种方法进行比较,同时保持恒定的坚硬性(高低),结果显示,拟议的方法在轨迹跟踪和产生互动力方面超越了其他两种情况,即使在出现意外的终端碰撞等扰动时也是如此。