A Reinforcement Learning Approach for Continuum Robot Control

Rigid joint manipulators are limited in their movement and degrees of freedom (DOF), while continuum robots possess a continuous backbone that allows for free movement and multiple DOF. Continuum robots move by bending over a section, taking inspiration from biological manipulators such as tentacles and trunks. This paper presents an implementation of a forward kinematics and velocity kinematics model to describe the planar continuum robot, along with the application of reinforcement learning (RL) as a control algorithm. In this paper, we have adopted the planar constant curvature representation for the forward kinematic modeling. This choice was made due to its straightforward implementation and its potential to fill the literature gap in the field RL-based control for planar continuum robots. The intended control mechanism is achieved through the use of Deep Deterministic Policy Gradient (DDPG), a RL algorithm that is suited for learning controls in continuous action spaces. After simulating the algorithm, it was observed that the planar continuum robot can autonomously move from any initial point to any desired goal point within the task space of the robot. By analyzing the results, we wanted to recommend a future direction for research in the field of continuum robot control, specifically in the application of RL algorithms. One potential area of focus could be the integration of sensory feedback, such as vision or force sensing, to improve the robot's ability to navigate complex environments. Additionally, exploring the use of different RL algorithms, such as Proximal Policy Optimization (PPO) or Trust Region Policy Optimization (TRPO), could lead to further advancements in the field. Overall, this paper demonstrates the potential for RL-based control of continuum robots and highlights the importance of continued research in this area.