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1.串联机器人动力学预训练。通过从模型维数、拓扑结构和动力学参数三个方面进行随机以产生大量异构串联机器人模型,再通过motor babbling随机若干运动轨迹,从而形成大规模的动力学轨迹数据集;利用Generative Pre-trained Transformer(GPT)结构学习大量机器人动力学以及三种目的不同的逆动力学模型。实验结果表明该方法在大规模轨迹测试集上具有较高的精度、较强的抗扰性以及泛化性,迁移到实际机器人时可以达到了与模型辨识相同的精度,并大幅降低了辨识过程的复杂度。



Other Abstract

Dynamics and controller learning are one of the main research directions in the field of robotics. Most of the existing methods use small-scale neural networks to train specifically for a target robot, but they have the shortcomings of poor generalization and low accuracy. With the proposal and development of pre-training technology, it has excellent performance in related downstream tasks. A question is that can it be used to improve the generalization of robot modeling and control and lower the entry barrier? To this end, this thesis introduces pre-training into the field of continuous robot control and studies the pre-training models of dynamics and controller of serial articulated robots.

The major contents of this thesis are summarized as follows:

1. Dynamics pre-training of serial articulated robots. A large number of heterogeneous serial articulated robot models are generated by randomizing dynamics parameters, topology configurations, and

model dimensions, and then a large-scale dynamics trajectory dataset is formed by randomizing motion trajectories through motor babbling method. A structure modified from the generative pre-trained transformer(GPT) is applied to study the dynamics of massive robots and three inverse dynamics models with different purposes. The experimental results on large-scale trajectory test dataset show that our method has high accuracy, good performance in disturbance rejection and generalization. When transferred to an actual robot, it achieves the same accuracy as model identification , and greatly reduces the identification process complexity.

2. Controller pre-training of serial articulated robots. Given an above-mentioned random robot model, we optimize a full state feedback controller for a random task and add random disturbances to obtain a control trajectory. Iteratively repeating this process results in a large-scale motion control trajectory dataset. We also adopt the GPT structure to learn the controller pre-training model of large-scale heterogeneous series robots, taking a random dynamics trajectory and motion control trajectories as input. The experimental results show that the model can realize the closed-loop control of unidentified robot and unknown tasks with only a priori dynamics trajectory and has high control accuracy, robustness, and generalization.

In summary, this thesis introduces pre-training into the field of robot continuous control, and constructs pre-training models of dynamics and controller, which not only shows high accuracy, robustness and generalization, but also lowers the entry barrier of related area.


Subject Area机器人控制
Document Type学位论文
Recommended Citation
GB/T 7714
李佳乐. 串联机器人动力学与控制器预训练[D]. 中国科学院大学人工智能学院. 中国科学院大学人工智能学院,2022.
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