The integration of physiological computing into mixed-initiative human-robot
interaction systems offers valuable advantages in autonomous task allocation by
incorporating real-time features as human state observations into the
decision-making system. This approach may alleviate the cognitive load on human
operators by intelligently allocating mission tasks between agents.
Nevertheless, accommodating a diverse pool of human participants with varying
physiological and behavioral measurements presents a substantial challenge. To
address this, resorting to a probabilistic framework becomes necessary, given
the inherent uncertainty and partial observability on the human's state. Recent
research suggests to learn a Partially Observable Markov Decision Process
(POMDP) model from a data set of previously collected experiences that can be
solved using Offline Reinforcement Learning (ORL) methods. In the present work,
we not only highlight the potential of partially observable representations and
physiological measurements to improve human operator state estimation and
performance, but also enhance the overall mission effectiveness of a
human-robot team. Importantly, as the fixed data set may not contain enough
information to fully represent complex stochastic processes, we propose a
method to incorporate model uncertainty, thus enabling risk-sensitive
sequential decision-making. Experiments were conducted with a group of
twenty-six human participants within a simulated robot teleoperation
environment, yielding empirical evidence of the method's efficacy. The obtained
adaptive task allocation policy led to statistically significant higher scores
than the one that was used to collect the data set, allowing for generalization
across diverse participants also taking into account risk-sensitive metrics.

将生理计算融入混合主动型人机交互系统中，通过将实时特征作为人体状态观察纳入决策系统，为自主任务分配提供了有价值的优势。通过在代理之间智能地分配任务来减轻人员认知负担。然而，应对具有不同生理和行为测量的多样化人员池的挑战较大。为了解决这个问题，需要采用概率框架，考虑到人的状态的内在不确定性和部分可观察性。最近的研究表明，可以从以前收集的经验数据集中学习部分可观察马尔科夫决策流程模型，并使用离线强化学习方法来解决该模型。在本研究中，我们不仅强调部分可观察表示和生理测量能够改进人员状态估计和绩效，而且还能增强人机团队的整体任务效果。值得注意的是，由于固定的数据集可能无法完整表示复杂的随机过程，因此我们提出了一种方法来考虑模型不确定性，从而实现风险敏感的序列决策。在模拟机器人远程操作环境下对 26 名参与者进行了实验，结果获得了该方法的实证证据。获得的自适应任务分配策略导致的得分显著高于用于收集数据集的策略，可以在多样化参与者之间进行推广，并考虑风险敏感的指标。