Parameters auto-tuning for biped robots in whole-body stabilization and active impedance control applications

This work proposes a parameters auto-tuning strategy for biped locomotion in whole-body stabilization control (inverse kinematics based and inverse dynamics based) and active impedance control based on Bayesian optimization(BO). Using the domain knowledge, the parameter space is divided into three sub-spaces and optimized by decoupling BO and alternating BO algorithms. The effectiveness of the proposed method is demonstrated in simulation using a torque-controlled biped robot that we developed. The 32 control parameters are tuned in less than 400 evaluations. In addition, the auto-tuned parameters are robust to different top-level velocity inputs and show compliant behavior with balance in push recovery scenarios. To the best of our knowledge, this is the first work to automatically tune the parameters of the three controllers (inverse kinematics, inverse dynamics and active impedance control) jointly.