Although in the literature it is common to find predictors and inference systems that try to predict human intentions, the uncertainty of these models due to the randomness of human behavior has led some authors to start advocating the use of communication systems that explicitly elicit human intention. In this work, it is analyzed the use of four different communication systems with a human-robot collaborative object transportation task as experimental testbed: two intention predictors (one based on force prediction and another with an enhanced velocity prediction algorithm) and two explicit communication methods (a button interface and a voice-command recognition system). These systems were integrated into IVO, a custom mobile social robot equipped with force sensor to detect the force exchange between both agents and LiDAR to detect the environment. The collaborative task required transporting an object over a 5-7 meter distance with obstacles in the middle, demanding rapid decisions and precise physical coordination. 75 volunteers perform a total of 255 executions divided into three groups, testing inference systems in the first round, communication systems in the second, and the combined strategies in the third. The results show that, 1) once sufficient performance is achieved, the human no longer notices and positively assesses technical improvements; 2) the human prefers systems that are more natural to them even though they have higher failure rates; and 3) the preferred option is the right combination of both systems.

翻译：尽管在文献中常见试图预测人类意图的预测器与推理系统，但由于人类行为随机性导致的模型不确定性，已促使部分学者开始倡导采用显式获取人类意图的通信系统。本研究以人机协作物体搬运任务为实验平台，分析了四种不同通信系统的性能：两种意图预测器（一种基于力预测，另一种采用增强型速度预测算法）与两种显式通信方法（按钮界面和语音指令识别系统）。这些系统被集成至IVO——一台配备力传感器（用于检测双方交互作用力）和激光雷达（用于环境感知）的定制移动社交机器人。协作任务要求将物体搬运5-7米距离，途中设有障碍物，需要快速决策与精确的物理协调。75名志愿者完成了总计255次实验，分为三组：第一轮测试推理系统，第二轮测试通信系统，第三轮测试组合策略。结果表明：1）当系统性能达到足够水平后，人类将不再关注且不会积极评价技术改进；2）尽管故障率更高，人类仍偏好更符合自身直觉的系统；3）最优方案是两类系统的恰当组合。