Abstract

A particular type of assistive robots designed for physical interaction with objects could play an important role assisting with mobility and fall prevention in healthcare facilities. Autonomous mobile manipulation presents a hurdle prior to safely being able to utilize robots in real life applications such as healthcare and warehouse robots. 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, equipment stands). We describe a probabilistic method for autonomous learning of an approximate dynamics model for these objects. In this method, we learn pre-categorized object models by using a small dataset consisting of force and motion interactions between the robot and object. In addition, we account for multiple manipulation strategies by formulating the manipulation planning as a mixed-integer convex optimization problem. The proposed manipulation framework considers the hybrid control system comprised of i) choosing which leg to grasp, and ii) continuous applied forces to move the object. We propose a manipulation planning 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 show results for several objects with different 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 can successfully move the object to a desired pose while avoiding collision.