论文标题
使用元学习神经差分方程的自适应异步控制
Adaptive Asynchronous Control Using Meta-learned Neural Ordinary Differential Equations
论文作者
论文摘要
基于模型的增强学习和控制已经在各种顺序决策问题域（包括机器人设置）中表现出巨大的潜力。但是，现实世界中的机器人系统通常会提出限制这些方法的适用性的挑战。特别是，我们注意到在许多工业系统中共同发生的两个问题：1）不规则/异步观察和动作以及2）环境动力学从一个情节到另一个情节的急剧变化（例如，有效载荷有效惯用属性不同）。我们提出了一个通用框架，该框架通过元学习自适应动力学模型来克服这些困难，以进行连续的时间预测和控制。所提出的方法是任务不合时宜的，可以直接向前的方式适应新任务。我们在两个不同的机器人模拟和实际工业机器人中进行了评估。
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.
