使用概率时间排序学习机器人超声扫描的奖励
Learning rewards for robotic ultrasound scanning using probabilistic temporal ranking
AI总结：
本文提出概率时间排序方法，从探索性演示中推断奖励函数，用于自主超声扫描的信息路径规划，改进现有奖励推断方法。
摘要：
信息路径规划是机器人视觉伺服和主动视点选择中一种成熟的方法，但通常假设已知合适的成本函数或目标状态。本文考虑逆问题，即任务目标未知，需要从演示者提供的探索性示例演示中推断奖励函数，用于下游的信息路径规划策略。不幸的是，由于演示的探索性质，许多现有的奖励推断策略不适用于这类问题。在本文中，我们提出了一种替代方法来处理这些次优的、探索性演示发生的问题。我们假设，在需要发现的任务中，任何演示的连续状态越来越可能与更高的奖励相关联，并利用这一假设生成基于时间的二元比较结果，在概率生成模型下推断支持这些排序的奖励函数。我们形式化了这种“概率时间排序”方法，并表明它改进了现有方法，用于自主超声扫描的奖励推断，这是医学成像中从演示学习的新应用，同时也广泛适用于面向目标的学习从演示任务。关键词：视觉伺服，奖励推断，概率时间排序
英文摘要：
Informative path-planning is a well established approach to visual-servoing and active viewpoint selection in robotics, but typically assumes that a suitable cost function or goal state is known. This work considers the inverse problem, where the goal of the task is unknown, and a reward function needs to be inferred from exploratory example demonstrations provided by a demonstrator, for use in a downstream informative path-planning policy. Unfortunately, many existing reward inference strategies are unsuited to this class of problems, due to the exploratory nature of the demonstrations. In this paper, we propose an alternative approach to cope with the class of problems where these sub-optimal, exploratory demonstrations occur. We hypothesise that, in tasks which require discovery, successive states of any demonstration are progressively more likely to be associated with a higher reward, and use this hypothesis to generate time-based binary comparison outcomes and infer reward functions that support these ranks, under a probabilistic generative model. We formalise this \emph{probabilistic temporal ranking} approach and show that it improves upon existing approaches to perform reward inference for autonomous ultrasound scanning, a novel application of learning from demonstration in medical imaging while also being of value across a broad range of goal-oriented learning from demonstration tasks. \keywords{Visual servoing \and reward inference \and probabilistic temporal ranking
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。
