Work

Deep Reinforcement Learning-Based Enhancement of Robotic Arm Target-Reaching Performance

Year: 2025

Type: article

Abstract: This work investigates the implementation of the Deep Deterministic Policy Gradient (DDPG) algorithm to enhance the target-reaching capability of the seven degree-of-freedom (7-DoF) Franka Pandarobotic arm. A simulated environment is established by employing OpenAI Gym, PyBullet, and Panda Gym. After 100,000 training time steps, the DDPG algorithm attains a success rate of 100% and an average reward of −1.8. The actor loss and critic loss values are 0.0846 and 0.00486, respectively, indicating improved decision-making and accurate value function estimations. The simulation results demonstrate the efficiency of DDPG in improving robotic arm performance, highlighting its potential for application to improve robotic arm manipulation.

Source: Actuators

DOI: 10.3390/act14040165

Authors: Ldet Honelign, Yoseph Abebe, Young Seok Jung, Abera Tullu, Sunghun Jung

Institutions: Addis Ababa Science and Technology University, Chosun University

Language: English

Topic: Reinforcement Learning in Robotics

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $2,896

Funders: Chosun University, Ministry of SMEs and Startups