A survey of multi-agent Human–Robot Interaction systems

Dahiya, Abhinav; Aroyo, Alexander Mois; Dautenhahn, Kerstin; Smith, Stephen L.

doi:10.1016/j.robot.2022.104335

Cited by 43 publications

(20 citation statements)

References 76 publications

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“…Related Works. A multi-human, multi-robot (MH-MR) team is a group consisting of multiple human agents and multiple robots working together to achieve a shared objective comprised of different tasks [2]. Such a team is highly heterogeneous in nature for the significant differences that exist among the human agents and the robots in terms of their capabilities, skills, and characteristics.…”

Section: Background and Preliminarymentioning

confidence: 99%

“…Human-robot teams offer significant benefits across various domains by combining the strengths of autonomous systems such as consistency and preciseness, with the creativity and adaptability associated with humans [1]. The increasing demand for more efficient solutions to complex and largescale tasks, such as disaster response, search and rescue, and environmental surveillance, has spurred the interest in multihuman multi-robot (MH-HR) teams [2]. The collaboration of multiple humans and robots with diverse capabilities, expertise, and characteristics involved in a MH-MR team presents great potential to enhance team complementarity, productivity, and versatility [3], [4].…”

Section: Introductionmentioning

confidence: 99%

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Initial Task Allocation for Multi-Human Multi-Robot Teams with Attention-based Deep Reinforcement Learning

Wang¹,

Zhao²,

Min³

2023

Preprint

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Multi-human multi-robot teams have great potential for complex and large-scale tasks through the collaboration of humans and robots with diverse capabilities and expertise.To efficiently operate such highly heterogeneous teams and maximize team performance timely, sophisticated initial task allocation strategies that consider individual differences across team members and tasks are required. While existing works have shown promising results in reallocating tasks based on agent state and performance, the neglect of the inherent heterogeneity of the team hinders their effectiveness in realistic scenarios. In this paper, we present a novel formulation of the initial task allocation problem in multi-human multirobot teams as contextual multi-attribute decision-make process and propose an attention-based deep reinforcement learning approach. We introduce a cross-attribute attention module to encode the latent and complex dependencies of multiple attributes in the state representation. We conduct a case study in a massive threat surveillance scenario and demonstrate the strengths of our model.

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Section: Background and Preliminarymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Initial Task Allocation for Multi-Human Multi-Robot Teams with Attention-based Deep Reinforcement Learning

Wang¹,

Zhao²,

Min³

2023

Preprint

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“…For Multi-agent HRI systems, they require more complex control strategies to coordinate several agents that may be dissimilar to one another (in roles, communication modes etc. ), can involve interactions that connect more than two agents at once, and need special attention to address any conflicts that may arise from their interactions [138]. Specifically, the human would give high-level commands to the robot group or control them individually using a screen-based interface, and robots need to adapt to the operator's actions or optimize some utility function (computational characteristics like task requirements, teaming needs, etc.)…”

Section: Human-robot Interactionmentioning

confidence: 99%

Research Status and Challenges on the Sustainable Development of Artificial Intelligence Courses from a Global Perspective

et al. 2023

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The widespread application of artificial intelligence technology in various fields has made the sustainable development of artificial intelligence courses an important direction in the field of artificial intelligence education and teaching. Therefore, it is particularly important to conduct an in-depth analysis of the current research status of “artificial intelligence courses” from a global perspective. Firstly, this article clarifies the three stages of slow development, rapid development, and mature development of artificial intelligence curriculum research through the number and distribution years of the literature. It also conducts a co-authorship analysis on the distribution of countries, institutions, and authors of artificial intelligence curriculum research and identifies countries, institutions, and core authors that have made greater contributions to artificial intelligence curriculum research. Secondly, due to the involvement of artificial intelligence in many different fields of knowledge, an analysis is conducted on the journals that published papers on artificial intelligence courses. Finally, based on the analysis of keyword density and time span, the current research hotspots of artificial intelligence courses are summarized: artificial intelligence technology empowerment courses, two education directions at different stages of artificial intelligence courses, and teaching forms in the field of artificial intelligence courses. The current research trend of artificial intelligence courses is analyzed from three aspects: teaching format, teaching content, and teaching objects. This article provides a theoretical reference value and practical basis for future research and development in the field of artificial intelligence courses, while also providing experience for the efficient and sustainable development of artificial intelligence courses to a certain extent.

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“…Multi-Agent Robot Exploration is a field of study that deals with finding optimal solutions for a group of robots exploring an unknown environment. The main challenge in this field is to coordinate the actions of multiple robots so that they can work together effectively and efficiently [ 1 , 2 , 3 , 4 , 5 , 6 , 7 , 8 , 9 , 10 , 11 , 12 ]. In order to solve this problem, researchers often use optimization algorithms, which are mathematical methods that find the best solution to a given problem based on specific objectives and constraints [ 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 ].…”

Section: Introductionmentioning

confidence: 99%

Multi-Agent Variational Approach for Robotics: A Bio-Inspired Perspective

et al. 2023

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This study proposes an adaptable, bio-inspired optimization algorithm for Multi-Agent Space Exploration. The recommended approach combines a parameterized Aquila Optimizer, a bio-inspired technology, with deterministic Multi-Agent Exploration. Stochastic factors are integrated into the Aquila Optimizer to enhance the algorithm’s efficiency. The architecture, called the Multi-Agent Exploration–Parameterized Aquila Optimizer (MAE-PAO), starts by using deterministic MAE to assess the cost and utility values of nearby cells encircling the agents. A parameterized Aquila Optimizer is then used to further increase the exploration pace. The effectiveness of the proposed MAE-PAO methodology is verified through extended simulations in various environmental conditions. The algorithm viability is further evaluated by comparing the results with those of the contemporary CME-Aquila Optimizer (CME-AO) and the Whale Optimizer. The comparison adequately considers various performance parameters, such as the percentage of the map explored, the number of unsuccessful runs, and the time needed to explore the map. The comparisons are performed on numerous maps simulating different scenarios. A detailed statistical analysis is performed to check the efficacy of the algorithm. We conclude that the proposed algorithm’s average rate of exploration does not deviate much compared to contemporary algorithms. The same idea is checked for exploration time. Thus, we conclude that the results obtained for the proposed MAE-PAO algorithm provide significant advantages in terms of enhanced map exploration with lower execution times and nearly no failed runs.

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A survey of multi-agent Human–Robot Interaction systems

Cited by 43 publications

References 76 publications

Initial Task Allocation for Multi-Human Multi-Robot Teams with Attention-based Deep Reinforcement Learning

Initial Task Allocation for Multi-Human Multi-Robot Teams with Attention-based Deep Reinforcement Learning

Research Status and Challenges on the Sustainable Development of Artificial Intelligence Courses from a Global Perspective

Multi-Agent Variational Approach for Robotics: A Bio-Inspired Perspective

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