Zhi Li

Chao Huang, Wenshuo Zang, Carlo Pinciroli et al.

Compared with single robots, Multi-Robot Systems (MRS) can perform missions more efficiently due to the presence of multiple members with diverse capabilities. However, deploying an MRS in wide real-world environments is still challenging due to uncertain and various obstacles (e.g., building clusters and trees). With a limited understanding of environmental uncertainty on performance, an MRS cannot flexibly adjust its behaviors (e.g., teaming, load sharing, trajectory planning) to ensure both environment adaptation and task accomplishments. In this work, a novel joint preference landscape learning and behavior adjusting framework (PLBA) is designed. PLBA efficiently integrates real-time human guidance to MRS coordination and utilizes Sparse Variational Gaussian Processes with Varying Output Noise to quickly assess human preferences by leveraging spatial correlations between environment characteristics. An optimization-based behavior-adjusting method then safely adapts MRS behaviors to environments. To validate PLBA's effectiveness in MRS behavior adaption, a flood disaster search and rescue task was designed. 20 human users provided 1764 feedback based on human preferences obtained from MRS behaviors related to "task quality", "task progress", "robot safety". The prediction accuracy and adaptation speed results show the effectiveness of PLBA in preference learning and MRS behavior adaption.

Jayam Patel, Tyagaraja Ramaswamy, Zhi Li et al.

How can multiple humans interact with multiple robots? The goal of our research is to create an effective interface that allows multiple operators to collaboratively control teams of robots in complex tasks. In this paper, we focus on a key aspect that affects our exploration of the design space of human-robot interfaces -- inter-human communication. More specifically, we study the impact of direct and indirect communication on several metrics, such as awareness, workload, trust, and interface usability. In our experiments, the participants can engage directly through verbal communication, or indirectly by representing their actions and intentions through our interface. We report the results of a user study based on a collective transport task involving 18 human subjects and 9 robots. Our study suggests that combining both direct and indirect communication is the best approach for effective multi-human / multi-robot interaction.

Jayam Patel, Tyagaraja Ramaswamy, Zhi Li et al.

Transparency is a key factor in improving the performance of human-robot interaction. A transparent interface allows humans to be aware of the state of a robot and to assess the progress of the tasks at hand. When multi-robot systems are involved, transparency is an even greater challenge, due to the larger number of variables affecting the behavior of the robots as a whole. Significant effort has been devoted to studying transparency when single operators interact with multiple robots. However, studies on transparency that focus on multiple human operators interacting with a multi-robot systems are limited. This paper aims to fill this gap by presenting a human-swarm interaction interface with graphical elements that can be enabled and disabled. Through this interface, we study which graphical elements are contribute to transparency by comparing four "transparency modes": (i) no transparency (no operator receives information from the robots), (ii) central transparency (the operators receive information only relevant to their personal task), (iii) peripheral transparency (the operators share information on each others' tasks), and (iv) mixed transparency (both central and peripheral). We report the results in terms of awareness, trust, and workload of a user study involving 18 participants engaged in a complex multi-robot task.