Understanding environment dynamics is necessary for robots to act safely and
optimally in the world. In realistic scenarios, dynamics are non-stationary and
the causal variables such as environment parameters cannot necessarily be
precisely measured or inferred, even during training. We propose Implicit
Identification for Dynamics Adaptation (IIDA), a simple method to allow
predictive models to adapt to changing environment dynamics. IIDA assumes no
access to the true variations in the world and instead implicitly infers
properties of the environment from a small amount of contextual data. We
demonstrate IIDA's ability to perform well in unseen environments through a
suite of simulated experiments on MuJoCo environments and a real robot dynamic
sliding task. In general, IIDA significantly reduces model error and results in
higher task performance over commonly used methods. Our code and robot videos
are at this https URL

本文提出了一种名为 IIDA 的简单方法，可以在没有真实环境变化情况下自适应环境动态。实验表明，IIDA 显著降低了模型误差，并提高了常用方法的任务性能，适用于机器人在真实场景中的应用。