Robotics Department, University of Michigan, Ann Arbor, MI, USA.
Military Institute of Engineering, Rio de Janeiro, Brazil.
Sci Rep. 2022 Sep 12;12(1):15304. doi: 10.1038/s41598-022-19140-5.
Effective human-robot collaboration requires the appropriate allocation of indivisible tasks between humans and robots. A task allocation method that appropriately makes use of the unique capabilities of each agent (either a human or a robot) can improve team performance. This paper presents a novel task allocation method for heterogeneous human-robot teams based on artificial trust from a robot that can learn agent capabilities over time and allocate both existing and novel tasks. Tasks are allocated to the agent that maximizes the expected total reward. The expected total reward incorporates trust in the agent to successfully execute the task as well as the task reward and cost associated with using that agent for that task. Trust in an agent is computed from an artificial trust model, where trust is assessed along a capability dimension by comparing the belief in agent capabilities with the task requirements. An agent's capabilities are represented by a belief distribution and learned using stochastic task outcomes. Our task allocation method was simulated for a human-robot dyad. The team total reward of our artificial trust-based task allocation method outperforms other methods both when the human's capabilities are initially unknown and when the human's capabilities belief distribution has converged to the human's actual capabilities. Our task allocation method enables human-robot teams to maximize their joint performance.
有效的人机协作需要在人和机器人之间合理分配不可分割的任务。一种能够充分利用每个主体（人类或机器人）独特能力的任务分配方法可以提高团队绩效。本文提出了一种基于人工信任的异构人机团队的新任务分配方法，机器人可以随着时间的推移学习代理能力，并分配现有和新的任务。任务分配给能够最大化预期总奖励的代理。预期的总奖励包含对代理成功执行任务的信任，以及与使用该代理执行该任务相关的任务奖励和成本。代理的信任是从人工信任模型中计算出来的，其中信任是通过比较对代理能力的信念与任务要求来沿着能力维度进行评估的。代理的能力由置信度分布表示，并使用随机任务结果进行学习。我们的任务分配方法对人类-机器人对进行了模拟。当人类的能力最初未知时，以及当人类的能力置信度分布收敛到人类的实际能力时，我们的基于人工信任的任务分配方法的团队总奖励优于其他方法。我们的任务分配方法使人机团队能够最大限度地提高他们的联合绩效。
