Multi-level control architecture for Bionic Handling Assistant robot augmented by learning from demonstration for apple-picking

The control of soft continuum robots is challenging owing to their mechanical elasticity and complex dynamics. An additional challenge emerges when we want to apply Learning from Demonstration (LfD) and need to collect necessary demonstrations due to the inherent control difficulty. In this paper, we provide a multi-level architecture from low-level control to high-level motion planning for the Bionic Handling Assistant (BHA) robot. We deploy learning across all levels to enable the application of LfD for a real-world manipulation task. To record the demonstrations, an actively compliant controller is used. A variant of dynamical systems' application that are able to encode both position and orientation then maps the recorded 6D end-effector pose data into a virtual attractor space. A recent LfD method encodes the pose attractors within the same model for point-to-point motion planning. In the proposed architecture, hybrid models that combine an analytical approach and machine learning techniques are used to overcome the inherent slow dynamics and model imprecision of the BHA. The performance and generalization capability of the proposed multi-level approach are evaluated in simulation and with the real BHA robot in an apple-picking scenario which requires high accuracy to control the pose of the robot's end-effector.

软体连续体机器人（soft continuum robots）的控制因其机械弹性与复杂动力学特性而极具挑战性。当我们希望应用演示学习（Learning from Demonstration, LfD）方法，且由于其固有的控制难度需要收集必要的演示样本时，又会额外面临一重挑战。本文针对仿生操作助手（Bionic Handling Assistant, BHA）机器人，提出了一套从底层控制到顶层运动规划的多层次架构。我们在所有层级部署学习机制，以实现将LfD方法应用于实际操控任务。为记录演示样本，本文采用主动柔顺控制器。一类可同时编码位置与姿态的动力学系统变体，将记录得到的六维末端执行器（end-effector）位姿数据映射至虚拟吸引子空间（virtual attractor space）。近期的一项LfD方法可在同一模型中编码用于点对点运动规划（point-to-point motion planning）的位姿吸引子。在所提出的架构中，我们采用融合解析方法（analytical approach）与机器学习技术（machine learning techniques）的混合模型（hybrid models），以克服BHA固有的动力学响应缓慢与建模精度不足的问题。所提出的多层次方法的性能与泛化能力，已通过仿真实验，以及在要求高精度控制机器人末端执行器位姿的苹果采摘场景（apple-picking scenario）中使用真实BHA机器人进行了验证。