Performing daily interaction tasks such as opening doors and pulling drawers in unstructured environments is a challenging problem for robots. The emergence of soft-bodied robots brings a new perspective to solving this problem. In this paper, inspired by humans performing interaction tasks through simple behaviors, we propose a hierarchical control system for soft arms, in which the low-level controller achieves motion control of the arm tip, the high-level controller controls the behaviors of the arm based on the low-level controller, and the top-level planner chooses what behaviors should be taken according to tasks. To realize the motion control of the soft arm in interacting with environments, we propose two control methods. The first is a feedback control method based on a simplified Jacobian model utilizing the motion laws of the soft arm that are not affected by environments during interaction. The second is a control method based on $\mathrm{Q}$-learning, in which we present a novel method to increase training data by setting virtual goals. We implement the hierarchical control system on a platform with the Honeycomb Pneumatic Networks Arm (HPN Arm) and validate the effectiveness of this system on a series of typical daily interaction tasks, which demonstrates this proposed hierarchical control system could render the soft arms to perform interaction tasks as simply as humans, without force sensors or accurate models of the environments. This work provides a new direction for the application of soft-bodied arms and offers a new perspective for the physical interactions between robots and environments.

在非结构化环境中执行日常交互任务，例如打开门和拉抽屉对于机器人来说是一个挑战性的问题。软体机器人的出现为解决这个问题带来了新的视角。在本文中，受人类通过简单行为执行交互任务的启发，我们提出了一种用于软臂的分层控制系统，其中低级控制器实现了手臂尖端的运动控制，而高级控制器则控制了手臂的行为。基于低层控制器，高层计划者根据任务选择应该采取的行为。为了实现与环境交互作用下软臂的运动控制，我们提出了两种控制方法。第一种是基于简化雅可比模型的反馈控制方法，该模型利用了在交互过程中不受环境影响的软臂运动定律。第二种是基于$\mathrm{问}$-学习中，我们提出了一种通过设置虚拟目标来增加训练数据的新颖方法。我们在具有蜂窝气动网络臂（HPN Arm）的平台上实现了分层控制系统，并在一系列典型的日常交互任务上验证了该系统的有效性，这表明该提议的分层控制系统可以使软臂执行交互像人类一样简单地完成任务，而无需使用力传感器或精确的环境模型。这项工作为软臂的应用提供了新的方向，并为机器人与环境之间的物理交互提供了新的视角。