Model Predictive Robot-Environment Interaction Control for Mobile Manipulation Tasks

Abstract

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 wholebody 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.

Model predictive impedance control with Gaussian processes for human and environment interaction

“…Contact can be considered as fully rigid constraints, using time-stepping approaches to resolve the contact impulse over an integrator time step [66], avoiding the issue of unbounded contact forces in rigid contact. A dynamic model for the environment has also been proposed for manipulation [67], relaxing kinematic constraints into a stiffness. In most contact planning situations, signed distance functions are needed-typically, this requires geometry information typically given by CAD, although it has been learned on simplified objects [68].…”

Model Predictive Control for Fluid Human-to-Robot Handovers

“…Model predictive control (MPC) is being increasingly used for reactive motion generation in manipulators [7], [14], [15], [16], [17]. Kuindersma et al [17] leveraged MPC to generate dynamic motions for humanoid robots, that can navigate varying terrains.…”

“…Faroni et al [14] explored leveraging MPC to dynamically slow down the robot when a human is in the workspace of the robot. Minniti et al [15] used MPC in combination with an observer to handle contact with the environment such as contact during opening of doors. Kramer et al [16] showed their approach avoiding dynamic obstacles leveraging non-linear programming to solve MPC.…”

Model Predictive Impedance Control with Gaussian Processes for Human and Environment Interaction

“…Contact can be considered as fully rigid constraints, using time-stepping approaches to resolve the contact impulse over an integrator time step [64], avoiding the issue of unbounded contact forces in rigid contact. A dynamic model for the environment has also been proposed for manipulation [65], relaxing kinematic constraints into a sti ness. In most contact planning situations, signed distance functions are needed to indicate the contact mode and give the contact Jacobian-this requires a geometric model typically given by CAD, although it has been learned on simplied objects [66].…”