Force Feedback Model-Predictive Control via Online Estimation

Nonlinear model-predictive control has recently shown its practicability in robotics. However it remains limited in contact interaction tasks due to its inability to leverage sensed efforts. In this work, we propose a novel model-predictive control approach that incorporates direct feedback from force sensors while circumventing explicit modeling of the contact force evolution. Our approach is based on the online estimation of the discrepancy between the force predicted by the dynamics model and force measurements, combined with high-frequency nonlinear model-predictive control. We report an experimental validation on a torque-controlled manipulator in challenging tasks for which accurate force tracking is necessary. We show that a simple reformulation of the optimal control problem combined with standard estimation tools enables to achieve state-of-the-art performance in force control while preserving the benefits of model-predictive control, thereby outperforming traditional force control techniques. This work paves the way toward a more systematic integration of force sensors in model predictive control.

Authors

• Armand Jordana
• Sébastien Kleff
• Justin Carpentier
• Nicolas Mansard
• Ludovic Righetti

Keywords

• Torque
• Systematics
• Force
• Force feedback
• Estimation
• Robot sensing systems
• Force sensors
• Model Predictive Control
• Online Estimation
• Dynamic Model
• Control Techniques
• Force Measurements
• Contact Force
• Tracking Accuracy
• Force Sensor
• Optimal Control Problem
• Force Control
• Direct Feedback
• Contact Interaction
• Reformulation Of Problem
• Nonlinear Model Predictive Control
• Model Predictive Control Approach
• Prediction Model
• Energy Minimization
• Cost Function
• Kalman Filter
• Integral Control
• Joint Torque
• Compensation Term
• Rigid Contact
• Circular Trajectory
• Normal Force
• Hard Constraints
• Contact Model
• Joint Space
• Task Space