APPLE：基于评价反馈的自适应规划器参数学习
APPLE: Adaptive Planner Parameter Learning from Evaluative Feedback
摘要：
经典的自主导航系统可以以无碰撞的方式控制机器人，通常具有可验证的安全性和可解释性。然而，当面对新的环境时，在系统能够如预期那样导航之前，通常需要专家对系统参数进行微调。为了减轻这一要求，最近提出的自适应规划参数学习范式允许机器人\emph{learn}如何动态调整规划参数使用远程操作的演示或纠正干预非专家用户。然而，这些交互方式要求用户完全控制移动机器人，这就要求用户熟悉机器人遥操作。作为替代方案，我们介绍\textsc{apple}，自适应规划参数学习从\emph{Evaluative Feedback}（实时，标量值的行为评估），这代表了一个要求较低的互动方式。仿真和物理实验表明，\textsc{apple}可以实现更好的性能相比，规划器与静态默认参数，甚至产生改进的学习参数，从更丰富的交互方式。
英文摘要：
Classical autonomous navigation systems can control robots in a collision-free manner, oftentimes with verifiable safety and explainability. When facing new environments, however, fine-tuning of the system parameters by an expert is typically required before the system can navigate as expected. To alleviate this requirement, the recently-proposed Adaptive Planner Parameter Learning paradigm allows robots to \emph{learn} how to dynamically adjust planner parameters using a teleoperated demonstration or corrective interventions from non-expert users. However, these interaction modalities require users to take full control of the moving robot, which requires the users to be familiar with robot teleoperation. As an alternative, we introduce \textsc{apple}, Adaptive Planner Parameter Learning from \emph{Evaluative Feedback} (real-time, scalar-valued assessments of behavior), which represents a less-demanding modality of interaction. Simulated and physical experiments show \textsc{apple} can achieve better performance compared to the planner with static default parameters and even yield improvement over learned parameters from richer interaction modalities.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
