The invention provides a
control system and method for learning variable impedance. The
system mainly comprises a variable impedance controller, a
system Gaussian process model, a variable
impedance control strategy, and a strategy learning
algorithm. The
system does not need any priori knowledge of an environment, and the system
Gaussian process model is constructed according to the interaction data. The long-term
inference and planning of the system is carried out in a Bayesian mode. The system can extract more useful information from the limited
observation data, and completes a complex force control task through the least
interaction time. Through the adding of an energy los item to a cost function, the system achieves the balance between an error and energy, and enables a
robot to begood in compliant capability. Finally, the obtained variable
impedance control strategy allows the target rigidness and damping parameters to be adjusted at the same time at different stages of the task. The system and method can be widely used for the compliant control tasks: double-mechanical-arm
assembly, multi-mechanical-arm cooperation and
robot gait control, and guarantee the safety and robustness of the interaction operation.