Data driven control of a continuum manipulator requires a lot of data for training but generating sufficient amount of real time data is not cost efficient. Random actuation of the manipulator can also be unsafe sometimes. Meta learning has been used successfully to adapt to a new environment. Hence, this paper tries to solve the above mentioned problem using meta learning. We consider two cases for that. First, this paper proposes a method to use simulation data for training the model using MAML(Model-Agnostic Meta-Learning). Then, it adapts to the real world using gradient steps. Secondly,if the simulation model is not available or difficult to formulate, then we propose a CGAN(Conditional Generative adversial network)-MAML based method for it. The model is trained using a small amount of real time data and augmented data for different loading conditions. Then, adaptation is done in the real environment. It has been found out from the experiments that the relative positioning error for both the cases are below 3%. The proposed models are experimentally verified on a real continuum manipulator.

翻译：连续体机械臂的数据驱动控制需要大量数据进行训练，但生成足够量的实时数据成本效率不高。机械臂的随机驱动有时也可能存在安全隐患。元学习已成功应用于适应新环境。因此，本文尝试利用元学习解决上述问题。我们考虑两种情况：首先，本文提出一种利用仿真数据通过MAML（模型无关元学习）训练模型的方法，然后通过梯度步长适应真实环境。其次，若仿真模型不可用或难以建立，我们提出一种基于CGAN（条件生成对抗网络）-MAML的方法。该模型使用少量实时数据和不同载荷条件下的增强数据进行训练，然后在真实环境中进行自适应。实验结果表明，两种情况的相对定位误差均低于3%。所提出的模型已在真实连续体机械臂上通过实验验证。