Model Predictive Robot-Environment Interaction Control for Mobile Manipulation Tasks

Modern, torque-controlled service robots can reg-
ulate contact forces when interacting with their environment.
Model Predictive Control (MPC) is a powerful method to solve
the underlying control problem, allowing to plan for whole-
body motions while including different constraints imposed by
the robot dynamics or its environment. However, an accurate
model of the robot-environment is needed to achieve a satisfying
closed-loop performance. Currently, this necessity undermines
the performance and generality of MPC in manipulation tasks.
In this work, we combine an MPC-based whole-body controller
with two adaptive schemes, derived from online system identi-
fication and adaptive control. As a result, we enable a general
mobile manipulator to interact with unknown environments,
without any need for re-tuning parameters or pre-modeling the
interacting objects. In combination with the MPC controller,
the two adaptive approaches are validated and benchmarked
with a ball-balancing manipulator in a door opening and object
lifting task.

Paper: https://arxiv.org/abs/2106.04202