Traditional dynamic models of continuum robots are in general computationally expensive and not suitable for real-time control. Recent approaches using learning-based methods to approximate the dynamic model of continuum robots for control have been promising, although real data hungry -- which may cause potential damage to robots and be time consuming -- and getting poorer performance when trained with simulation data only. This paper presents a model-based learning framework for continuum robot closed-loop control that, by combining simulation and real data, shows to require only 100 real data to outperform a real-data-only controller trained using up to 10000 points. The introduced data-efficient framework with three control policies has utilized a Gaussian process regression (GPR) and a recurrent neural network (RNN). Control policy A uses a GPR model and a RNN trained in simulation to optimize control outputs for simulated targets; control policy B retrains the RNN in policy A with data generated from the GPR model to adapt to real robot physics; control policy C utilizes policy A and B to form a hybrid policy. Using a continuum robot with soft spines, we show that our approach provides an efficient framework to bridge the sim-to-real gap in model-based learning for continuum robots.
翻译:连续机器人的传统动态模型一般在计算上成本高昂,不适于实时控制。最近采用学习方法对连续机器人动态模型进行近似于连续机器人动态模型进行控制的方法是很有希望的,尽管实际数据饥饿 -- -- 可能对机器人造成潜在损害并耗费时间 -- -- 而且在仅接受模拟数据培训时性能更差。本文为连续机器人闭路控制提供了一个基于模型的学习框架,通过将模拟数据与真实数据相结合,显示只需要100个真实数据,才能超过一个经过训练的、使用最多10000点的实际数据控制器。引入了三个控制政策的数据效率框架,使用了高斯进程回归(GPR)和一个经常性神经网络(RNN)。控制政策A使用GPR模型和一个经过模拟培训的RNN,以优化模拟目标的控制产出;控制政策B在政策A中用GPR模型生成的数据重新输入RNN,以适应真实的机器人物理学;控制政策C利用政策A和B形成混合政策。使用软脊盖的连续机器人,我们展示了一种高效的框架,用以在模拟中连接Simto-to的机器人。