A Trajectory Optimisation-Based Incremental Learning Strategy for Learning from Demonstration

Yuqi Wang

doi:10.3390/app14114943

2024-06-06

Yuqi Wang

李卫东 (Weidong Li) *

Yuchen Liang

DOI：10.3390/app14114943

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摘要

En 中文

The insufficient generalisation capability of the conventional learning from demonstration (LfD) model necessitates redemonstrations. In addition, retraining the model can overwrite existing knowledge, making it impossible to perform previously acquired skills in new application scenarios. These are not economical and efficient. To address the issues, in this study, a broad learning system (BLS) and probabilistic roadmap (PRM) are integrated with dynamic movement primitive (DMP)-based LfD. Three key innovations are proposed in this paper: (1) segmentation and extended demonstration: a 1D-based topology trajectory segmentation algorithm (1D-SEG) is designed to divide the original demonstration into several segments. Following the segmentation, a Gaussian probabilistic roadmap (G-PRM) is proposed to generate an extended demonstration that retains the geometric features of the original demonstration. (2) DMP modelling and incremental learning updating: BLS-based incremental learning for DMP (Bi-DMP) is performed based on the constructed DMP and extended demonstration. With this incremental learning approach, the DMP is capable of self-updating in response to task demands, preserving previously acquired skills and updating them without training from scratch. (3) Electric vehicle (EV) battery disassembly case study: this study developed a solution suitable for EV battery disassembly and established a decommissioned battery disassembly experimental platform. Unscrewing nuts and battery cell removal are selected to verify the effectiveness of the proposed algorithms based on the battery disassembly experimental platform. In this study, the effectiveness of the algorithms designed in this paper is measured by the success rate and error of the task execution. In the task of unscrewing nuts, the success rate of the classical DMP is 57.14% and the maximum error is 2.760 mm. After the optimisation of 1D-SEG, G-PRM, and Bi-DMP, the success rate of the task is increased to 100% and the maximum error is reduced to 1.477 mm.

Keyword:

learning from demonstration (LfD)

dynamic movement primitives (DMP)

incremental learning

broad learning system (BLS)

probabilistic roadmap (PRM)