论文标题
使用路径积分控制的参数不确定性下的基于模型的概括
Model-Based Generalization Under Parameter Uncertainty Using Path Integral Control
论文作者
论文摘要
这项工作解决了需要在线控制和适应的复杂环境中机器人互动的问题。通过在路径积分控制的自由能公式中扩展样品空间，我们将自然扩展推导到路径积分控制中，将不确定性嵌入到动作中，并为基于模型的机器人计划提供鲁棒性。我们的算法使用不同的机器人应用于各种任务，并在模拟和现实世界实验中验证我们的结果。我们进一步表明，我们的方法能够实时运行而不会丧失性能。可以在https://sites.google.com/view/emppi上找到实验的视频以及其他实施详细信息。
This work addresses the problem of robot interaction in complex environments where online control and adaptation is necessary. By expanding the sample space in the free energy formulation of path integral control, we derive a natural extension to the path integral control that embeds uncertainty into action and provides robustness for model-based robot planning. Our algorithm is applied to a diverse set of tasks using different robots and validate our results in simulation and real-world experiments. We further show that our method is capable of running in real-time without loss of performance. Videos of the experiments as well as additional implementation details can be found at https://sites.google.com/view/emppi.
