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Description
I'm trying to create a simple optimization tool that estimates a pre-planned sequence of target configurations that I can then track in real-time with MPC using the Direct Optimization interface, but I've found the documentation to be lacking, particularly in providing practical implementation examples that users can start from when designing their own DO strategies.
In my particular case, for instance, as a minimal starting point to allow me to better understand the practical workflow of the Direct API, I want to track a short reference kinematic/joint trajectory for a robot that is equipped with position-controlled actuators and associated joint position sensors (ignoring force tracking at the moment).
Pre-optimization, I populate sensor_measurement with my target joint positions, initialize configuration and configuration_previous with the same values and add noise as done in the unit testing code.
Optimization converges to perfect match between sensor_prediction matches sensor_measurement after a single iteration, but configuration holds rather different values, which I am having trouble understanding why, considering position-controlled joints + join position sensors should lead to a direct correspondence (or at least similar values) with the reference sensor measurement
What exactly am I missing here?