Learning With Dual Demonstration Domains: Random Domain-Adaptive Meta-Learning

Hu, Ziye; Li, Wei; Gan, Zhongxue; Guo, Weikun; Zhu, Jiwei; Gao, Xiang; Yang, Xuyun; Peng, Yueyan; Zuo, Zhihao; Wen, Jiang; Decheng, Zhou

doi:10.1109/lra.2022.3145088

Cited by 6 publications

(1 citation statement)

References 33 publications

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“…In recent years, robotics and artificial intelligence have developed rapidly, and robots now play an indispensable role in factory workshops or daily life [1][2][3]. Traditional industrial robotic arms are designed for heavy loads and huge operating spaces.…”

Section: Introductionmentioning

confidence: 99%

Research on LFD System of Humanoid Dual-Arm Robot

Cui,

Kou,

Chen

et al. 2024

Symmetry

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Although robots have been widely used in a variety of fields, the idea of enabling them to perform multiple tasks in the same way that humans do remains a difficulty. To solve this, we investigate the learning from demonstration (LFD) system with our independently designed symmetrical humanoid dual-arm robot. We present a novel action feature matching algorithm. This algorithm accurately transforms human demonstration data into task models that robots can directly execute, considerably improving LFD’s generalization capabilities. In our studies, we used motion capture cameras to capture human demonstration actions, which included combinations of simple actions (the action layer) and a succession of complicated operational tasks (the task layer). For the action layer data, we employed Gaussian mixture models (GMM) for processing and constructing an action primitive library. As for the task layer data, we created a “keyframe” segmentation method to transform this data into a series of action primitives and build another action primitive library. Guided by our algorithm, the robot successfully imitated complex human tasks. Results show its excellent task learning and execution, providing an effective solution for robots to learn from human demonstrations and significantly advancing robot technology.

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Section: Introductionmentioning

confidence: 99%