Multi-robot Cooperative Assembly Task Planning for Large-scale Aerospace Structures

Lin, Jiamei; Li, Pengcheng; Jia-long, Dai; Liu, Shaorui; Tian, Wei; Li, Bo; Wang, Changrui

doi:10.1109/yac57282.2022.10023897

Cited by 2 publications

(1 citation statement)

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“…This approach simplifies learning by segmenting it into geometry structure estimation, trajectory planning, and uncertainty handling (refer to Figure 1). It is crucial to recognize that manipulation depends on the geometric constraints of the task, the grasp pose of the slave object, and the master object's location (Li et al, 2023). Assuming the known master object's location, we can represent the motion trajectory and assembly relationship with a lowdimensional framework, facilitating skill adaptation across various poses.…”

Section: Introductionmentioning

confidence: 99%

Extended residual learning with one-shot imitation learning for robotic assembly in semi-structured environment

Wang,

Su,

Sun

et al. 2024

Front. Neurorobot.

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IntroductionRobotic assembly tasks require precise manipulation and coordination, often necessitating advanced learning techniques to achieve efficient and effective performance. While residual reinforcement learning with a base policy has shown promise in this domain, existing base policy approaches often rely on hand-designed full-state features and policies or extensive demonstrations, limiting their applicability in semi-structured environments.MethodsIn this study, we propose an innovative Object-Embodiment-Centric Imitation and Residual Reinforcement Learning (OEC-IRRL) approach that leverages an object-embodiment-centric (OEC) task representation to integrate vision models with imitation and residual learning. By utilizing a single demonstration and minimizing interactions with the environment, our method aims to enhance learning efficiency and effectiveness. The proposed method involves three key steps: creating an object-embodiment-centric task representation, employing imitation learning for a base policy using via-point movement primitives for generalization to different settings, and utilizing residual RL for uncertainty-aware policy refinement during the assembly phase.ResultsThrough a series of comprehensive experiments, we investigate the impact of the OEC task representation on base and residual policy learning and demonstrate the effectiveness of the method in semi-structured environments. Our results indicate that the approach, requiring only a single demonstration and less than 1.2 h of interaction, improves success rates by 46% and reduces assembly time by 25%.DiscussionThis research presents a promising avenue for robotic assembly tasks, providing a viable solution without the need for specialized expertise or custom fixtures.

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Section: Introductionmentioning

confidence: 99%

Extended residual learning with one-shot imitation learning for robotic assembly in semi-structured environment

Wang,

Su,

Sun

et al. 2024

Front. Neurorobot.

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A Systematic Literature Review on Multi-Robot Task Allocation

K A,

Subramaniam

2024

ACM Comput. Surv.

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Muti-Robot system is gaining attention and is one of the critical areas of research when it comes to robotics. Coordination among multiple robots and how different tasks are allocated to different system agents are being studied. The objective of this Systematic Literature Review (SLR) is to provide insights on the recent advancement in Multi Robot Task Allocation(MRTA) problems emphasizing promising approaches for task allocation. In this study, we collected scientific papers from 5 different databases for MRTA. We outline the different approaches for task allocation algorithms, classifying them according to the methods, and emphasizing recent advances. In addition, we discuss the function of uncertainty in task allocation and typical coordination techniques utilized in task allocation to identify gaps in the literature and suggest the most promising ones.

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Multi-robot Cooperative Assembly Task Planning for Large-scale Aerospace Structures

Cited by 2 publications

References 20 publications

Extended residual learning with one-shot imitation learning for robotic assembly in semi-structured environment

Extended residual learning with one-shot imitation learning for robotic assembly in semi-structured environment

A Systematic Literature Review on Multi-Robot Task Allocation

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