标题： REALM：人机交互中学到的模型的实时辅助估计 显示英文标题

标题： REALM: Real-Time Estimates of Assistance for Learned Models in Human-Robot Interaction

摘要： 有许多机制（即，输入类型）可以促进实时人类互动，从而实现有效的人机团队协作。 例如，先前的研究表明，远程操作、纠正性和离散性（即，在少量选择中表达偏好）的输入如何使机器人能够完成复杂任务。 然而，很少有先前的工作研究过结合不同的方法，特别是机器人根据其对任务的理解来估计和引导最有效的帮助形式的机会。 在本文中，我们提出了一种方法，基于机器人策略的动作不确定性来估计不同类型人类辅助机制的价值。 我们的核心思想是构建数学表达式，用于计算随机机器人策略在交互后预期的微分熵（即，不确定性），从而比较不同交互的预期价值。 由于每种类型的人类输入都会对人类参与度提出不同的要求，我们展示了如何将微分熵估计与可能性惩罚方法相结合，以有效地平衡反馈信息需求与所需的输入水平。 我们通过仿真和机器人用户研究展示了我们的方法如何与新兴学习模型（例如扩散模型）结合，从而生成准确的辅助价值估计。 我们的用户研究结果显示，所提出的方案可以通过最少的人类反馈实现不确定机器人行为的任务完成。 显示英文摘要

摘要： There are a variety of mechanisms (i.e., input types) for real-time human interaction that can facilitate effective human-robot teaming. For example, previous works have shown how teleoperation, corrective, and discrete (i.e., preference over a small number of choices) input can enable robots to complete complex tasks. However, few previous works have looked at combining different methods, and in particular, opportunities for a robot to estimate and elicit the most effective form of assistance given its understanding of a task. In this paper, we propose a method for estimating the value of different human assistance mechanisms based on the action uncertainty of a robot policy. Our key idea is to construct mathematical expressions for the expected post-interaction differential entropy (i.e., uncertainty) of a stochastic robot policy to compare the expected value of different interactions. As each type of human input imposes a different requirement for human involvement, we demonstrate how differential entropy estimates can be combined with a likelihood penalization approach to effectively balance feedback informational needs with the level of required input. We demonstrate evidence of how our approach interfaces with emergent learning models (e.g., a diffusion model) to produce accurate assistance value estimates through both simulation and a robot user study. Our user study results indicate that the proposed approach can enable task completion with minimal human feedback for uncertain robot behaviors.