Periodic movement learning in a soft-robotic arm

Paris Oikonomou , Mehdi Khamassi (1) , Costas S Tzafestas

1 ISIR - Institut des Systèmes Intelligents et de Robotique

In this paper we introduce a novel technique that aims to dynamically control a modular bio-inspired soft-robotic arm in order to perform cyclic rhythmic patterns. Oscillatory signals are produced at the actuator's level by a central pattern generator (CPG), resulting in the generation of a periodic motion by the robot's end-effector. The proposed controller is based on a model-free neurodynamic scheme and is assigned with the task of training a policy that computes the parameters of the CPG model which generates a trajectory with desired features. The proposed methodology is first evaluated with a simulation model, which successfully reproduces the trained targets. Then experiments are also conducted using the real robot. Both procedures validate the efficiency of the learning architecture to successfully complete these tasks.

Mots clés: Reinforcement learning; Central pattern generators; Soft Robotics; Rhythmic movements

Domaines: Automatique / Robotique; Machine Learning [stat.ML]

Paris Oikonomou, Mehdi Khamassi, Costas S Tzafestas. Periodic movement learning in a soft-robotic arm. IEEE International Conference on Robotics and Automation (ICRA 2020), May 2020, Paris (virtuel), France. ⟨10.1109/ICRA40945.2020.9197035⟩. ⟨hal-03435441⟩