Assistive robots designed for physical interaction with objects will play an important role in assisting with mobility and fall prevention in healthcare facilities. Autonomous mobile manipulation presents a hurdle prior to safely using robots in real-life applications. In this article, we introduce a mobile manipulation framework based on model predictive control using learned dynamics models of objects. We focus on the specific problem of manipulating legged objects such as those commonly found in healthcare environments and personal dwellings (e.g., walkers, tables, chairs). We describe a probabilistic method for autonomous learning of an approximate dynamics model for these objects. In this method, we learn dynamic parameters using a small dataset consisting of force and motion data from interactions between the robot and object. Moreover, we account for multiple manipulation strategies by formulating manipulation planning as a mixed-integer convex optimization. The proposed framework considers the hybrid control system composed of (i) choosing which leg to grasp and (ii) control of continuous applied forces for manipulation. We formalize our algorithm based on model predictive control to compensate for modeling errors and find an optimal path to manipulate the object from one configuration to another. We present results for several objects with various wheel configurations. Simulation and physical experiments show that the obtained dynamics models are sufficiently accurate for safe and collision-free manipulation. When combined with the proposed manipulation planning algorithm, the robot successfully moves the object to the desired pose while avoiding any collision.

设计用于与对象进行物理交互的辅助机器人将在协助医疗机构的移动性和防止跌倒方面发挥重要作用。在实际应用中安全使用机器人之前，自主移动操纵提出了一个障碍。在本文中，我们介绍了一种基于模型预测控制的移动操作框架，该模型使用学习的对象动力学模型进行了模型预测控制。我们专注于处理有腿物体的特定问题，例如在医疗保健环境和个人住宅（例如，步行者，桌子，椅子）中常见的那些物体。我们描述了一种用于这些对象的近似动力学模型的自主学习的概率方法。在这种方法中，我们使用小型数据集学习动态参数，该小型数据集由来自机器人与物体之间的相互作用的力和运动数据组成。而且，我们通过将操纵计划表述为混合整数凸优化来考虑多种操纵策略。提出的框架考虑了混合控制系统，该混合控制系统包括（i）选择要抓握的腿和（ii）控制连续施加的力进行操纵。我们基于模型预测控制对算法进行形式化，以补偿建模错误，并找到一条最佳途径来操纵对象从一种配置到另一种配置。我们介绍了具有不同车轮配置的多个对象的结果。仿真和物理实验表明，所获得的动力学模型对于安全且无碰撞的操纵是足够准确的。与建议的操纵计划算法结合使用时，机器人可以成功地将对象移动到所需姿势，同时避免任何碰撞。