A Framework for Real-World Multi-Robot Systems Running Decentralized GNN-Based Policies

Blumenkamp, Jan; Morad, Steven; Gielis, Jennifer; Li, Qingbiao; Prorok, Amanda

doi:10.1109/icra46639.2022.9811744

Cited by 18 publications

(12 citation statements)

References 23 publications

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“…Only the nearby robots share their state information and observations with each other. Blumenkamp et al [ 111 ] developed a framework for decentralized coordination of an MRS. The RL aspect of their system uses GNNs and PPO.…”

Section: Multi-robot System Applications Of Multi-agent Deep Reinforc...mentioning

confidence: 99%

Multi-Agent Deep Reinforcement Learning for Multi-Robot Applications: A Survey

Orr

Dutta

2023

Sensors

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Deep reinforcement learning has produced many success stories in recent years. Some example fields in which these successes have taken place include mathematics, games, health care, and robotics. In this paper, we are especially interested in multi-agent deep reinforcement learning, where multiple agents present in the environment not only learn from their own experiences but also from each other and its applications in multi-robot systems. In many real-world scenarios, one robot might not be enough to complete the given task on its own, and, therefore, we might need to deploy multiple robots who work together towards a common global objective of finishing the task. Although multi-agent deep reinforcement learning and its applications in multi-robot systems are of tremendous significance from theoretical and applied standpoints, the latest survey in this domain dates to 2004 albeit for traditional learning applications as deep reinforcement learning was not invented. We classify the reviewed papers in our survey primarily based on their multi-robot applications. Our survey also discusses a few challenges that the current research in this domain faces and provides a potential list of future applications involving multi-robot systems that can benefit from advances in multi-agent deep reinforcement learning.

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Section: Multi-robot System Applications Of Multi-agent Deep Reinforc...mentioning

confidence: 99%

Multi-Agent Deep Reinforcement Learning for Multi-Robot Applications: A Survey

Orr

Dutta

2023

Sensors

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“…The state-of-the-art in achieving resilience in agent systems have mostly focussed on decentralized control architectures (Ghedini et al, 2018;Sartoretti et al, 2019;Zhang et al, 2020;Blumenkamp et al, 2022). As discussed by Iocchi et al (2001), a decentralized architecture could be distinguished from a centralized architecture based on how the decision-making process is designed.…”

Section: Resilience In Autonomous Agent Systemsmentioning

confidence: 99%

Counterfactual learning in enhancing resilience in autonomous agent systems

Samarasinghe

2023

Front. Artif. Intell.

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Resilience in autonomous agent systems is about having the capacity to anticipate, respond to, adapt to, and recover from adverse and dynamic conditions in complex environments. It is associated with the intelligence possessed by the agents to preserve the functionality or to minimize the impact on functionality through a transformation, reconfiguration, or expansion performed across the system. Enhancing the resilience of systems could pave way toward higher autonomy allowing them to tackle intricate dynamic problems. The state-of-the-art systems have mostly focussed on improving the redundancy of the system, adopting decentralized control architectures, and utilizing distributed sensing capabilities. While machine learning approaches for efficient distribution and allocation of skills and tasks have enhanced the potential of these systems, they are still limited when presented with dynamic environments. To move beyond the current limitations, this paper advocates incorporating counterfactual learning models for agents to enable them with the ability to predict possible future conditions and adjust their behavior. Counterfactual learning is a topic that has recently been gaining attention as a model-agnostic and post-hoc technique to improve explainability in machine learning models. Using counterfactual causality can also help gain insights into unforeseen circumstances and make inferences about the probability of desired outcomes. We propose that this can be used in agent systems as a means to guide and prepare them to cope with unanticipated environmental conditions. This supplementary support for adaptation can enable the design of more intelligent and complex autonomous agent systems to address the multifaceted characteristics of real-world problem domains.

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“…Practical communication links suffer from message dropouts, asynchronous and out-oforder reception, and decentralized mesh topologies that may not offer reliability guarantees. Since multi-robot policies are typically trained in a synchronous fashion, these factors are hard to capture and simulate [148]. Furthermore, very few studies have captured any of these network effects in a large-scale setting [21].…”

Section: Challenges and Open Problemsmentioning

confidence: 99%

A Critical Review of Communications in Multi-robot Systems

2022

Self Cite

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Purpose of Review This review summarizes the broad roles that communication formats and technologies have played in enabling multi-robot systems. We approach this field from two perspectives: of robotic applications that need communication capabilities in order to accomplish tasks, and of networking technologies that have enabled newer and more advanced multi-robot systems. Recent Findings Through this review, we identify a dearth of work that holistically tackles the problem of co-design and co-optimization of robots and the networks they employ. We also highlight the role that data-driven and machine learning approaches play in evolving communication pipelines for multi-robot systems. In particular, we refer to recent work that diverges from hand-designed communication patterns, and also discuss the “sim-to-real” gap in this context. Summary We present a critical view of the way robotic algorithms and their networking systems have evolved, and make the case for a more synergistic approach. Finally, we also identify four broad Open Problems for research and development, while offering a data-driven perspective for solving some of them.

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A Framework for Real-World Multi-Robot Systems Running Decentralized GNN-Based Policies

Cited by 18 publications

References 23 publications

Multi-Agent Deep Reinforcement Learning for Multi-Robot Applications: A Survey

Multi-Agent Deep Reinforcement Learning for Multi-Robot Applications: A Survey

Counterfactual learning in enhancing resilience in autonomous agent systems

A Critical Review of Communications in Multi-robot Systems

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