In human-robot teams where agents collaborate together, there needs to be a
clear allocation of tasks to agents. Task allocation can aid in achieving the
presumed benefits of human-robot teams, such as improved team performance. Many
task allocation methods have been proposed that include factors such as agent
capability, availability, workload, fatigue, and task and domain-specific
parameters. In this paper, selected work on task allocation is reviewed. In
addition, some areas for continued and further consideration in task allocation
are discussed. These areas include level of collaboration, novel tasks, unknown
and dynamic agent capabilities, negotiation and fairness, and ethics. Where
applicable, we also mention some of our work on task allocation. Through
continued efforts and considerations in task allocation, human-robot teaming
can be improved.

本文回顾了任务分配领域的一些研究工作，讨论了任务分配中需要继续研究的方向，包括协作水平、新的任务、未知和动态的代理能力、谈判和公平以及伦理问题。通过不断的努力和研究，人机协作团队的表现和表现可以得到改进。

人机团队作业分配的考虑因素

Considerations for Task Allocation in Human-Robot Teams

The challenge of language grounding is to fully understand natural language
by grounding language in real-world referents. While AI techniques are
available, the widespread adoption and effectiveness of such technologies for
human-robot teams relies critically on user trust. This survey provides three
contributions relating to the newly emerging field of trust in language
grounding, including a) an overview of language grounding research in terms of
AI technologies, data sets, and user interfaces; b) six hypothesised trust
factors relevant to language grounding, which are tested empirically on a
human-robot cleaning team; and c) future research directions for trust in
language grounding.

该论文探讨了 “语言基础” 这一挑战，旨在通过将语言绑定到真实世界的参照物中来完全理解自然语言。该研究提供了三个贡献，分别为：a）分析了人工智能技术、数据集和用户界面等方面的语言基础研究概述；b）提出并实证了与语言基础相关的六个假设的信任因素，该实验是在人机清洁组中进行的；以及 c）探讨了信任语言基础的未来研究方向。

语言基础的信任：人机协作中的新 AI 挑战

Trust in Language Grounding: a new AI challenge for human-robot teams

In human-robot teams, humans often start with an inaccurate model of the
robot capabilities. As they interact with the robot, they infer the robot's
capabilities and partially adapt to the robot, i.e., they might change their
actions based on the observed outcomes and the robot's actions, without
replicating the robot's policy. We present a game-theoretic model of human
partial adaptation to the robot, where the human responds to the robot's
actions by maximizing a reward function that changes stochastically over time,
capturing the evolution of their expectations of the robot's capabilities. The
robot can then use this model to decide optimally between taking actions that
reveal its capabilities to the human and taking the best action given the
information that the human currently has. We prove that under certain
observability assumptions, the optimal policy can be computed efficiently. We
demonstrate through a human subject experiment that the proposed model
significantly improves human-robot team performance, compared to policies that
assume complete adaptation of the human to the robot.

研究人机协作中的不完全适应现象，提出一种基于博弈论的人机部分适应模型，实验结果显示该模型显著提高了人机团队的性能。