论文标题
上下文是一切：动态适应的隐性识别
Context is Everything: Implicit Identification for Dynamics Adaptation
论文作者
论文摘要
了解机器人在世界上安全和最佳行动是必要的。在现实的情况下，动态是非平稳的，即使在训练期间，也不必精确地测量或推断出因果变量。我们提出了动态适应性（IIDA）的隐式识别，这是一种允许预测模型适应不断变化的环境动态的简单方法。 IIDA无法访问世界上的真实变化，而是从少量的上下文数据中隐含地侵入环境的属性。我们通过在Mujoco环境和真正的机器人动态滑动任务上进行了一系列模拟实验，证明了IIDA在看不见的环境中表现良好的能力。通常，IIDA显着降低了模型误差，并且与常用方法相比，任务性能更高。我们的代码和机器人视频位于https://bennevans.github.io/iida/
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 https://bennevans.github.io/iida/
