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
this https URL

本研究在复杂环境中研究了机器人交互问题，通过扩展路径积分控制的自由能公式中的样本空间，提出了一种嵌入不确定性的路径积分控制方法，为基于模型的机器人规划提供了鲁棒性，实验证明了其能够在不损失性能的情况下实时运行。

路径积分控制下参数不确定性的基于模型的泛化

Model-Based Generalization Under Parameter Uncertainty Using Path  Integral Control

In this paper we address the problem to compute state dependent feedback
controls for path integral control problems. To this end we generalize the path
integral control formula and utilize this to construct parameterized state
dependent feedback controllers. In addition, we show a novel relation between
control and importance sampling: better control, in terms of control cost,
yields more efficient importance sampling, in terms of effective sample size.
The optimal control provides a zero-variance estimate.

本文提出了利用广义路径积分控制公式计算状态相关反馈控制器来解决路径积分控制问题的方法，并阐述了控制成本和有效采样大小之间的关系。最优控制器提供了零方差估计。