@workshop{Munir2022,
title = {Sharing Autonomy of Exploration and Exploitation via Control Interface},
author = {Aiman Munir and Ramviyas Parasuraman},
url = {https://sites.google.com/view/saphri-icra2022/contributions},
year = {2022},
date = {2022-05-23},
booktitle = {ICRA 2022 Workshop on Shared Autonomy in Physical Human-Robot Interaction: Adaptability and Trust},
abstract = {Shared autonomy is a control paradigm that refers to the adaptation of a robot’s autonomy level in dynamic environments while taking human intentions and status into account at the same time. Here, the autonomy level can be changed based on internal/external information and human input. However, there are no clear guidelines and studies that help understand ”when” should a robot adapt its autonomy level to different functionalities. Therefore, in this paper, we create a framework that helps to improve the human-robot control interface by allowing humans to adapt to the robots’ autonomy level as well as to create a study design to gather insights into human’s preference to switch autonomy levels based on the current situation. We create two high-level strategies - Exploration to gather more data
and Exploitation to make use of current data - for a search and rescue task. These two strategies can be achieved with human inputs or autonomous algorithms. We intend to understand the human preferences to the autonomy levels (and ”when” they want to switch) to these two strategies. The analysis is expected to provide insights into designing shared autonomy schemes and algorithms to consider human preferences in adaptively using autonomy levels of certain high-level strategies.},
keywords = {autonomy, human-robot interface, trust},
pubstate = {published},
tppubtype = {workshop}
}

@conference{Yang2021,
title = {How Can Robots Trust Each Other For Better Cooperation? A Relative Needs Entropy Based Robot-Robot Trust Assessment Model},
author = {Qin Yang and Ramviyas Parasuraman},
doi = {10.1109/SMC52423.2021.9659187},
year = {2021},
date = {2021-10-20},
booktitle = {2021 IEEE International Conference on Systems, Man, and Cybernetics (SMC)},
pages = {2656--2663},
publisher = {IEEE},
organization = {IEEE},
abstract = {Cooperation in multi-agent and multi-robot systems can help agents build various formations, shapes, and patterns presenting corresponding functions and purposes adapting to different situations. Relationships between agents such as their spatial proximity and functional similarities could play a crucial role in cooperation between agents. Trust level between agents is an essential factor in evaluating their relationships' reliability and stability, much as people do. This paper proposes a new model called Relative Needs Entropy (RNE) to assess trust between robotic agents. RNE measures the distance of needs distribution between individual agents or groups of agents. To exemplify its utility, we implement and demonstrate our trust model through experiments simulating a heterogeneous multi-robot grouping task in a persistent urban search and rescue mission consisting of tasks at two levels of difficulty. The results suggest that RNE trust-Based grouping of robots can achieve better performance and adaptability for diverse task execution compared to the state-of-the-art energy-based or distance-based grouping models.},
keywords = {evaluation, multi-robot, planning, trust},
pubstate = {published},
tppubtype = {conference}
}