Model-based Reinforcement Learning and Control have demonstrated great potential in various sequential decision making problem domains, including in robotics settings. However, real-world robotics systems often present challenges that limit the applicability of those methods. In particular, we note two problems that jointly happen in many industrial systems: 1) Irregular/asynchronous observations and actions and 2) Dramatic changes in environment dynamics from an episode to another (e.g. varying payload inertial properties). We propose a general framework that overcomes those difficulties by meta-learning adaptive dynamics models for continuous-time prediction and control. The proposed approach is task-agnostic and can be adapted to new tasks in a straight-forward manner. We present evaluations in two different robot simulations and on a real industrial robot.
@article{arxiv.2207.12062,
title = {Adaptive Asynchronous Control Using Meta-learned Neural Ordinary Differential Equations},
author = {Achkan Salehi and Steffen Rühl and Stephane Doncieux},
journal= {arXiv preprint arXiv:2207.12062},
year = {2023}
}