  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.
