Model Predictive Robot-Environment Interaction Control for Mobile Manipulation Tasks
This work addresses the challenge of improving robot adaptability in manipulation tasks for service robots, though it appears incremental by building on existing MPC and adaptive control methods.
The paper tackled the problem of enabling mobile manipulators to interact with unknown environments without requiring accurate pre-modeling or parameter tuning, by combining Model Predictive Control with adaptive schemes, achieving successful performance in tasks like door opening and object lifting.
Modern, torque-controlled service robots can regulate 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 identification 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 door opening and object lifting tasks.