LTC21: Learning to control

Plan

• Week 1 : Tutorials and invited talks on control and robotic. Setup software for projects.

• Week 2-3: Project work by participants along with invited talks.

We target live demonstration of the project results at the final presentation to accompany the theoretical and experimental results on learned control.

Projects

1. Open-loop stochastic sampling via MPPI vs MPPI combined with some form of gradient descent - which can find the optimum MPC plan with less computation? (Marcin Paluch, Frederik Heetmeyer, Florian Bolli, Krishna Kidambi)

2. What is the optimum loss function for training DNN model of cartpole dynamics? (Marcin Paluch and Frederik Heetmeyer)

3. What is the best lap time that can be achieved in L2RACE car racing simulation with learned car dynamics? (Florian Bolli, Antonio Rios, and Tobi Delbruck)

4. Can we control a physical cartpole robot with MPC and with MPPI? And once we manage that using the dymamical systems model, can we do it with a model trained from data? (Asude Aydin and Antonio Rios, Marcin and Frederik)

5. Can we control a wheeled robot with unique morphology (Xiang Deng)

Team

• Marcin Paluch (PhD ETHZ)

• Antonio Rios (U Seville postdoc lecturer)

• Krishna Kidambi (U Maryland postdoc)

• Nikhil Garg (BITS Pilani/USherbrooke masters student)

• Ante Maric (U Zagreb masters student)

• Frederik Heetmeyer (ETHZ masters student)

• Florian Bolli (ETHZ masters student)

• Gerald Wunsching (TU Munich)

• Asude Aydin (Masters student, NSC program, UZH-ETH Zurich)

• Xiang Deng (UPenn GRASP Lab alumni)

Current status

In 2019 and 2020 our Telluride topic areas “CDS19: Controlling Dynamical Systems” and “LTC20: Learning to Control” explored a number of basic ideas and systems. The projects were basic, but resulted in the ICRA 2019 conference paper on controlling Caltech's AMBER lab AMPRO with the EdgeDRNN accelerator.

Since the online 2020 workshop the LTC group has been meeting weekly with the goal of learning dynamics from data and using the learned model with model-predictive control (MPC). We have had more than 25 meetings since the workshop. We are learning a lot about the practicalities of MPC and learning models in RNNs.