Robots operating alongside humans often encounter unfamiliar environments that make autonomous task completion challenging. Though improving models and increasing dataset size can enhance a robot's performance in unseen environments, data collection and model refinement may be impractical in every environment. Approaches that utilize human demonstrations through manual operation can aid in refinement and generalization, but often require significant data collection efforts to generate enough demonstration data to achieve satisfactory task performance. Interactive approaches allow for humans to provide correction to robot action in real time, but intervention policies are often based on explicit factors related to state and task understanding that may be difficult to generalize. Addressing these challenges, we train a lightweight interaction policy that allows robots to decide when to proceed autonomously or request expert assistance at estimated times of uncertainty. An implicit estimate of uncertainty is learned via evaluating the feature extraction capabilities of the robot's visual navigation policy. By incorporating part-time human interaction, robots recover quickly from their mistakes, significantly improving the odds of task completion. Incorporating part-time interaction yields an increase in success of 0.38 with only a 0.3 expert interaction rate within the Habitat simulation environment using a simulated human expert. We further show success transferring this approach to a new domain with a real human expert, improving success from less than 0.1 with an autonomous agent to 0.92 with a 0.23 human interaction rate. This approach provides a practical means for robots to interact and learn from humans in real-world settings.

翻译：机器人在与人类协同工作时，常会遇到陌生环境，这使得自主完成任务变得困难。尽管改进模型和增加数据集规模可以提升机器人在未知环境中的性能，但在每个环境中进行数据收集和模型优化可能并不现实。通过手动操作利用人类示范的方法有助于优化和泛化，但通常需要大量的数据收集工作以生成足够的示范数据来实现满意的任务性能。交互式方法允许人类实时纠正机器人动作，但干预策略往往基于与状态和任务理解相关的显式因素，这些因素可能难以泛化。针对这些挑战，我们训练了一个轻量级的交互策略，使机器人能够在估计的不确定性时刻决定是自主进行还是请求专家协助。通过评估机器人视觉导航策略的特征提取能力，学习到对不确定性的隐式估计。通过引入部分时间的人类交互，机器人能够快速从错误中恢复，显著提高任务完成的概率。在Habitat仿真环境中使用模拟人类专家，引入部分时间交互使成功率提高了0.38，而专家交互率仅为0.3。我们进一步展示了将该方法迁移到新领域并使用真实人类专家的成功案例，将成功率从自主智能体的不足0.1提升至0.92，人类交互率为0.23。该方法为机器人在现实环境中与人类交互并从中学习提供了一种实用途径。