Adaptive Asynchronous Control Using Meta-Learned Neural Ordinary Differential Equations

摘要

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: first, irregular/asynchronous observations and actions and, second, dramatic changes in environment dynamics from an episode to another (e.g <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">.<inline-formula><tex-math notation="LaTeX">$,$</tex-math></inline-formula></i> 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.