Learning to control from expert demonstrations

03/09/2022
by   Alimzhan Sultangazin, et al.
0

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.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
10/01/2022

Bayesian Q-learning With Imperfect Expert Demonstrations

Guided exploration with expert demonstrations improves data efficiency f...
research
07/11/2018

Learning Singularity Avoidance

With the increase in complexity of robotic systems and the rise in non-e...
research
12/07/2022

ICT4S2022 – Demonstrations and Posters Track Proceedings

Submissions accepted for The 8th International Conference on ICT for Sus...
research
01/04/2021

Robust Maximum Entropy Behavior Cloning

Imitation learning (IL) algorithms use expert demonstrations to learn a ...
research
09/21/2022

LMI-based Variable Impedance Controller design from User Demonstrations and Preferences

In this paper, we introduce a new off-line method to find suitable param...
research
07/28/2020

Learning Stable Manoeuvres in Quadruped Robots from Expert Demonstrations

With the research into development of quadruped robots picking up pace, ...
research
12/20/2021

Demonstration Informed Specification Search

This paper considers the problem of learning history dependent task spec...

Please sign up or login with your details

Forgot password? Click here to reset