Abstract
Trajectory planning is an important stage in robot operation. Many imitation learning methods have been researched for learning operation skills from demonstrated trajectories. However, it is still a challenge to use the learned skill models to generate motion trajectories suitable for various changing conditions. In this paper, a closed-loop dynamical evaluation and optimization mechanism is proposed for imitation learning model to generate the optimal trajectories that can adapt to multiple conditions. This mechanism works by integrating the following parts: (1) imitation learning based on an improved dynamic motion primitive; (2) constructing the trajectory similarity evaluation function; (3) presenting an enhanced whale optimization algorithm(EWOA) by introducing the piecewise decay rate and inertia weight for avoiding getting stuck in local optima. The EWOA iteratively optimizes the key parameter of the skill learning model based on the cost function of the trajectory similarity evaluation for generating the trajectory with the highest similarity to the teaching trajectory. The effectiveness of the EWOA is validated using 10 functions by comparing with the other two methods. And the feasibility of the dynamical optimization mechanism is proved under different motion primitives and various generation conditions.
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Acknowledgements
The research leading to these results received funding from Scientific Research Project of Beijing Educational Committee (KM202110005023), National Science Foundation of China (62273012, 62373016,62373012) and Beijing Natural Science Foundation (4212033).
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Chunfang Liu was responsible for the overall planning and revision of the paper, Changfeng Li was responsible for the program writing and experimental data collation and analysis, Xiaoli Li was responsible for the thought guidance of the paper, Guoyu Zuo was responsible for the revision of the paper and Pan Yu was responsible for the revision of the document format of the paper.
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Liu, C., Li, C., Li, X. et al. An effective dynamical evaluation and optimization mechanism for accurate motion primitives learning. Appl Intell 55, 209 (2025). https://doi.org/10.1007/s10489-024-06147-w
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DOI: https://doi.org/10.1007/s10489-024-06147-w