# Learning to control from expert demonstrations

arXiv: https://arxiv.org/abs/2203.05012 · HTML: https://mlsanity.com/paper/2203.05012/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 49 references that are in the map.
- First posted on arXiv 9 Mar 2022.

## Builds on (most cited first, 9 of 9)

- [Convex optimization](https://mlsanity.com/paper/2106.01946/) (2021)
- [End to End Learning for Self-Driving Cars](https://mlsanity.com/paper/1604.07316/) (2016)
- [End-to-end Driving via Conditional Imitation Learning](https://mlsanity.com/paper/1710.02410/) (2017)
- [Learning by Cheating](https://mlsanity.com/paper/1912.12294/) (2019)
- [A Review of Robot Learning for Manipulation: Challenges, Representations, and Algorithms](https://mlsanity.com/paper/1907.03146/) (2019)
- [Deep Drone Acrobatics](https://mlsanity.com/paper/2006.05768/) (2020)
- [On Imitation Learning of Linear Control Policies: Enforcing Stability and Robustness Constraints via LMI Conditions](https://mlsanity.com/paper/2103.12945/) (2021)
- [Fitting a Linear Control Policy to Demonstrations with a Kalman Constraint](https://mlsanity.com/paper/2001.07572/) (2020)
- [Data-driven Stabilization of SISO Feedback Linearizable Systems](https://mlsanity.com/paper/2003.14240/) (2020)

## Abstract (arXiv)

In this paper, we revisit the problem of learning a stabilizing controller from a finite number of demonstrations by an expert. By first focusing on feedback linearizable systems, we show how to combine expert demonstrations into a stabilizing controller, provided that demonstrations are sufficiently long and there are at least n+1 of them, where n is the number of states of the system being controlled. When we have more than n+1 demonstrations, we discuss how to optimally choose the best n+1 demonstrations to construct the stabilizing controller. We then extend these results to a class of systems that can be embedded into a higher-dimensional system containing a chain of integrators. The feasibility of the proposed algorithm is demonstrated by applying it on a CrazyFlie 2.0 quadrotor.