mlsanityData to 7 Oct 2026

A Dataset Schema for Cooperative Learning from Demonstration in Multi-robots Systems

arXiv 1912.01741 ↗First posted 3 Dec 2019cs.RO cs.AI cs.MAcited by 1

On the map: Offline RL and imitation learning from data → Robot learning from demonstration

Abstract (arXiv)

Multi-Agent Systems (MASs) have been used to solve complex problems that demand intelligent agents working together to reach the desired goals. These Agents should effectively synchronize their individual behaviors so that they can act as a team in a coordinated manner to achieve the common goal of the whole system. One of the main issues in MASs is the agents' coordination, being common domain experts observing MASs execution disapprove agents' decisions. Even if the MAS was designed using the best methods and tools for agents' coordination, this difference of decisions between experts and MAS is confirmed. Therefore, this paper proposes a new dataset schema to support learning the coordinated behavior in MASs from demonstration. The results of the proposed solution are validated in a Multi-Robot System (MRS) organizing a collection of new cooperative plans recommendations from the demonstration by domain experts.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Placed in Robot learning from demonstration, one of 13 directions in Offline RL and imitation learning from data. Builds on 1 of its 43 references that are in the map. First posted on arXiv 3 Dec 2019.

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1 of 43 references.

References · 1

  1. 2013Clustering on Multiple Incomplete Datasets via Collective Kernel Learningcited by 11

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Citing papers per year: 2024: 1.

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  1. 2024Learning from Demonstration Framework for Multi-Robot Systems Using Interaction Keypoints and Soft Actor-Critic Methodscited by 0

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