标题： 稳健的机器人导航策略搜索 显示英文标题

标题： Robust Policy Search for Robot Navigation

摘要： 复杂机器人导航和控制问题可以表述为策略搜索问题。 然而，在不确定环境中进行交互式学习可能成本较高，需要使用数据高效的方法。 贝叶斯优化是一种高效的非线性优化方法，其中查询是经过仔细选择的，以收集关于最优位置的信息。 这是通过一个代理模型实现的，该模型编码了过去的信息，并通过查询选择的获取函数。 贝叶斯优化对输入数据或先验假设中的不确定性非常敏感。 在本工作中，我们结合了鲁棒优化和统计鲁棒性，表明这两种类型的鲁棒性是协同的。 对于鲁棒优化，我们使用了一种改进的无迹贝叶斯优化版本，在策略不确定性存在的情况下提供了安全且可重复的策略。 我们还提供了新的理论见解。 对于统计鲁棒性，我们使用了一个自适应代理模型，并引入了玻尔兹曼选择作为一种随机获取方法，以确保收敛性并在代理建模误差存在时提高性能。 我们在多个优化基准和机器人任务中展示了结果。 显示英文摘要

摘要： Complex robot navigation and control problems can be framed as policy search problems. However, interactive learning in uncertain environments can be expensive, requiring the use of data-efficient methods. Bayesian optimization is an efficient nonlinear optimization method where queries are carefully selected to gather information about the optimum location. This is achieved by a surrogate model, which encodes past information, and the acquisition function for query selection. Bayesian optimization can be very sensitive to uncertainty in the input data or prior assumptions. In this work, we incorporate both robust optimization and statistical robustness, showing that both types of robustness are synergistic. For robust optimization we use an improved version of unscented Bayesian optimization which provides safe and repeatable policies in the presence of policy uncertainty. We also provide new theoretical insights. For statistical robustness, we use an adaptive surrogate model and we introduce the Boltzmann selection as a stochastic acquisition method to have convergence guarantees and improved performance even with surrogate modeling errors. We present results in several optimization benchmarks and robot tasks.