Efficient Force Control Learning System for Industrial Robots Based on Variable Impedance Control

Abstract

Learning variable impedance control is a powerful method to improve the performance of force control. However, current methods typically require too many interactions to achieve good performance. Data-inefficiency has limited these methods to learn force-sensitive tasks in real systems. In order to improve the sampling efficiency and decrease the required interactions during the learning process, this paper develops a data-efficient learning variable impedance control method that enables the industrial robots automatically learn to control the contact force in the unstructured environment. To this end, a Gaussian process model is learned as a faithful proxy of the system, which is then used to predict long-term state evolution for internal simulation, allowing for efficient strategy updates. The effects of model bias are reduced effectively by incorporating model uncertainty into long-term planning. Then the impedance profiles are regulated online according to the learned humanlike impedance strategy. In this way, the flexibility and adaptivity of the system could be enhanced. Both simulated and experimental tests have been performed on an industrial manipulator to verify the performance of the proposed method.

Variable Impedance Control and Learning—A Review

“…In the context of VIC, Li et al ( 2018 , 2019 ) used Gaussian processes (Williams and Rasmussen, 2006 ) to learn a probabilistic representation of the interaction dynamics. In order to overcome the measurement noise of the force/torque sensor, Li et al ( 2018 ) designed a Kalman filter to estimate the actual interaction forces.…”

“…In the context of VIC, Li et al ( 2018 , 2019 ) used Gaussian processes (Williams and Rasmussen, 2006 ) to learn a probabilistic representation of the interaction dynamics. In order to overcome the measurement noise of the force/torque sensor, Li et al ( 2018 ) designed a Kalman filter to estimate the actual interaction forces. The learned model is used to make long-term reward prediction and optimize the policy using gradient-based optimization as originally proposed by Deisenroth et al ( 2015 ).…”

The Advanced Control Approach based on SMC Design for the High-Fidelity Haptic Power Lever of a Small Hybrid Electric Aircraft

“…The impedance control approach [124][125][126] is employed when a mechatronic device is in contact with its environment or with a human. It is an important control concept in modern robotics that is demonstrated by numerous applications [127][128][129][130][131][132][133][134][135][136][137][138][139][140]. The mechanical impedance, which features a force response of a body or a system of bodies on imposed velocity or position, can be described as a force-velocity relationship or force-position relationship.…”

Neural Network Based Contact Force Control Algorithm for Walking Robots

“…NN-based hybrid position/force control was proposed by Passold [ 30 ] and Kumar [ 31 ]. Moreover, observer based approaches have been reported in [ 32 , 33 , 34 ]. However, these researches also used a force sensor in their model.…”