论文标题
基于数据有效的模型学习框架，用于连续机器人的闭环控制
A Data-Efficient Model-Based Learning Framework for the Closed-Loop Control of Continuum Robots
论文作者
论文摘要
连续机器人的传统动态模型通常在计算上昂贵，不适合实时控制。使用基于学习的方法近似于控制连续机器人的动态模型的最新方法是有希望的，尽管实际的数据饥饿（可能会对机器人造成潜在的损害并耗时），并且仅在使用模拟数据培训时就会获得较差的性能。本文介绍了一个基于模型的学习框架，用于连续机器人闭环控制，该框架通过结合模拟和真实数据，显示仅需要100个真实数据即可超越最多可使用10000点的纯DATA控制器。带有三个控制策略的数据效率框架已采用高斯过程回归（GPR）和经常性神经网络（RNN）。控制策略A使用GPR模型和经过模拟训练的RNN来优化模拟目标的控制输出；控制策略B在策略A中的RNN重新培训，其数据具有从GPR模型生成的数据以适应真正的机器人物理；控制策略C利用策略A和B形成混合政策。使用带有软刺的连续机器人，我们表明我们的方法提供了一个有效的框架，以弥合基于模型的Continuum Robots基于模型的学习差距。
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.
