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[D] Interesting papers for RNN problem / Time-Series prediction with largely _known_ underlying actuators?

A bit of background: For my master thesis will I predict the inverse dynamics of a robotic arm by matching the measured position, speed and acceleration of each joint to the measured torque applied to the respective joint. The measured torque can – for the purpose of this machine learning task – be seen as the ground truth and is currently determined through feedback controllers measuring how much the actual trajectory is off in comparison to the intended trajectory (though it would be advantageous to know this beforehand, hence the ML application). This basically makes the problem a supervised learning problem.

For a quick visualization: see the following graphs: I have to match the [position, speed and acceleration] of a 7 joint robot to the [applied torque] (the blue line is the actual measurement of applied torque to the first joint, the others are quick drafts of ML prediction systems). If you would like to read more about this problem, I can recommend this recent paper

Now to my issue: As I understand it, does this problem seem like a typical time-series problem well suited for RNNs. However all obvious underlying actuators (position, speed, acceleration) that will influence the predicted torque are known, making it therefore unnecessary for RNNs to detect some underlying time dependent pattern and therefore turning my problem into a relatively simple nondiscrete classification problem (I hope this is the correct term) – correct? However the non-obvious and hardly- or non measurable underlying actuators (e.g. friction, inertia, deflection, etc), that cause inverse dynamics prediction to be a Machine learning problem in the first place, may (or may not…) be time dependent. Considering this is the problem still a viable RNN problem as I understand it, even though the underlying actuators are largely known.

My question: Aside from me being very happy with you checking my logic (and also general feedback), would I also appreciate any links/keywords to research that looks into that RNN problem / Time series prediction with the twist that the underlying actuators are mostly known. I would also appreciate any links/keywords to recent research in the field of nondiscrete classificaiton that is promising, as I will approach the inverse dynamics problem as a nondiscrete classification problem as well as a time series prediction problem and compare the viability of both over the course of my thesis. I also have more or less read most research that specifically looks into inverse dynamics prediction problem, though I am hoping to get good research that looks at this problem in a more general way or looks at a similar problem in another application domain, so that I might employ it for the inverse dynamics prediction task.

Thank you for taking the time of reading my question, I very much appreciate it!

submitted by /u/OnePaulToRuleThemAll
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Toronto AI is a social and collaborative hub to unite AI innovators of Toronto and surrounding areas. We explore AI technologies in digital art and music, healthcare, marketing, fintech, vr, robotics and more. Toronto AI was founded by Dave MacDonald and Patrick O'Mara.