Human-robot teaming is one of the most important applications of artificial
intelligence in the fast-growing field of robotics. For effective teaming, a
robot must not only maintain a behavioral model of its human teammates to
project the team status, but also be aware that its human teammates'
expectation of itself. Being aware of the human teammates' expectation leads to
robot behaviors that better align with human expectation, thus facilitating
more efficient and potentially safer teams. Our work addresses the problem of
human-robot cooperation with the consideration of such teammate models in
sequential domains by leveraging the concept of plan explicability. In plan
explicability, however, the human is considered solely as an observer. In this
paper, we extend plan explicability to consider interactive settings where
human and robot behaviors can influence each other. We term this new measure as
Interactive Plan Explicability. We compare the joint plan generated with the
consideration of this measure using the fast forward planner (FF) with the plan
created by FF without such consideration, as well as the plan created with
actual human subjects. Results indicate that the explicability score of plans
generated by our algorithm is comparable to the human plan, and better than the
plan created by FF without considering the measure, implying that the plans
created by our algorithms align better with expected joint plans of the human
during execution. This can lead to more efficient collaboration in practice.

本文考虑了人机团队合作的问题，并基于计划可解释性的概念，将其扩展到考虑交互设置。结果表明，我们的算法生成的计划的可解释度得分与人类计划相当，并且比不考虑该度量的 FF 计划更好，从而更好地满足人类期望，提高人机合作效率。

人机协作中的交互式计划可解释性

Interactive Plan Explicability in Human-Robot Teaming

Intelligent robots and machines are becoming pervasive in human populated
environments. A desirable capability of these agents is to respond to
goal-oriented commands by autonomously constructing task plans. However, such
autonomy can add significant cognitive load and potentially introduce safety
risks to humans when agents behave unexpectedly. Hence, for such agents to be
helpful, one important requirement is for them to synthesize plans that can be
easily understood by humans. While there exists previous work that studied
socially acceptable robots that interact with humans in "natural ways", and
work that investigated legible motion planning, there lacks a general solution
for high level task planning. To address this issue, we introduce the notions
of plan {\it explicability} and {\it predictability}. To compute these
measures, first, we postulate that humans understand agent plans by associating
abstract tasks with agent actions, which can be considered as a labeling
process. We learn the labeling scheme of humans for agent plans from training
examples using conditional random fields (CRFs). Then, we use the learned model
to label a new plan to compute its explicability and predictability. These
measures can be used by agents to proactively choose or directly synthesize
plans that are more explicable and predictable to humans. We provide
evaluations on a synthetic domain and with human subjects using physical robots
to show the effectiveness of our approach

针对智能机器人的高水平任务计划存在的问题，提出了 “计划可解释性” 和 “可预测性” 的概念，通过条件随机场模型学习标签方案并对新计划进行标签，用以提高机器人计划的可解释性和可预测性，该方法经过了人机交互和实际机器人测试并取得了较好的性能。