让机器人通过动作传递隐含信号，提升人机协作搬运效率。
Implicit Communication in Human-Robot Collaborative Transport
- 用动作编码隐性沟通信号，实现人机协同策略推断
- 实验显示团队表现优于基线，人类感知机器人更流畅可靠
- 适合研究人机协作、具身智能与自然交互的学者
我们研究人-机器人协同搬运任务，即机器人与用户共同将物体移动至目标位姿。在缺乏显式通信的情况下，该任务极具挑战性，因异构代理（人与机器人）需在感知、执行与推理能力差异显著的前提下实现紧密的隐性协调。核心洞察在于：双方可通过影响被搬运物体状态的动作，编码细微的沟通信号。为此，我们设计了一种概率推断机制，将双人协同动作观测映射为工作空间遍历的联合策略集合。基于此，定义了表征人类对策略演化不确定性的代价函数，并引入模型预测控制器中，实现不确定性最小化与效率最大化的平衡。我们在移动操作臂（Hello Robot Stretch）上部署该框架，并在24名参与者的一致性实验中验证。结果表明，本框架显著提升了团队性能，且相比无沟通机制的基线，机器人被感知为更流畅、更可靠的协作伙伴。
原文摘要 · Abstract (English)
We focus on human-robot collaborative transport, in which a robot and a user collaboratively move an object to a goal pose. In the absence of explicit communication, this problem is challenging because it demands tight implicit coordination between two heterogeneous agents, who have very different sensing, actuation, and reasoning capabilities. Our key insight is that the two agents can coordinate fluently by encoding subtle, communicative signals into actions that affect the state of the transported object. To this end, we design an inference mechanism that probabilistically maps observations of joint actions executed by the two agents to a set of joint strategies of workspace traversal. Based on this mechanism, we define a cost representing the human's uncertainty over the unfolding traversal strategy and introduce it into a model predictive controller that balances between uncertainty minimization and efficiency maximization. We deploy our framework on a mobile manipulator (Hello Robot Stretch) and evaluate it in a within-subjects lab study (N=24). We show that our framework enables greater team performance and empowers the robot to be perceived as a significantly more fluent and competent partner compared to baselines lacking a communicative mechanism.
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