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.

Controlling flexible and continuously structured continuum robots is a challenging task in the field of robotics and control systems. This study explores the use of reinforcement learning (RL) algorithms in controlling a three-section planar continuum robot. The study aims to investigate the impact of various reward functions on the performance of the RL algorithm. The RL algorithm utilized in this study is the Deep Deterministic Policy Gradient (DDPG), which can be applied to both continuous-state and continuous-action problems. The study’s findings reveal that the design of the RL environment, including the selection of reward functions, significantly influences the performance of the RL algorithm. The study provides significant information on the design of RL environments for the control of continuum robots, which may be valuable to researchers and practitioners in the field of robotics and control systems.

This work presents a reinforcement learning framework for controlling a planar threesection continuum robot in environments with static obstacles. Assuming constant curvature for each section, the robot is trained to navigate toward a fixed goal while avoiding collisions with multiple static objects. A custom simulation environment was developed to support three levels of scenario difficulty, easy, medium and hard, each with varying obstacle density and placement. The learning process is driven by the Deep Deterministic Policy Gradient (DDPG) algorithm, which enables smooth and continuous curvature control. Careful attention was paid to the design of the reward function and the network architecture, both of which were critical to achieving stable and reliable policy learning. Performance was evaluated across multiple runs, revealing that the agent successfully generalized its behavior across scenarios of increasing complexity. The proposed framework demonstrates the potential of reinforcement learning as a viable approach to safe and adaptive control in continuum robotic systems, with promising implications for applications such as medical navigation, search and rescue, and inspection in confined environments.