  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 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 the manipulation planning as a mixed-integer convex optimization.
The proposed framework considers the hybrid control system comprised 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 show 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 a desired pose
while avoiding collision.
