Actuator Model, Identification and Differential Dynamic Programming for a TALOS Humanoid Robot

In this experimental paper, we would like to validate a non linear optimal control solver to realize torque control on actuators embedded in a TALOS humanoid robot. The targeted application involves high payload, thus, it is necessary to handle the mechanical limitations of the system. To this extent, we propose a method to model, identify and control the TALOS humanoid actuators. The model includes the actuator drive chain and the corresponding inertial parameters that are identified at once using two experimental dataset. The identified model is then used by a Differential Dynamic Programming (DDP) optimal control solver to take into account the actuator limits. We demonstrated that the DDP can decrease the quality of the tracking to avoid physical limits in angular position, velocity and current in extreme conditions such as carrying large loads. Because of the solver high computational time, we validate our method on one actuator of the robot, the elbow joint, using its main CPU. In the experiments, we charge up to 34 kg on the arm of the robot at 5cm of the elbow joint, corresponding to 16 N at the joint level. The proposed implementation is working on this specific joint at 300μs and provide an effective solution to a real-world control problem. In the future, we will implement it over dedicated and embedded electronics board attached to each actuator.