International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 12 Issue: 04 | Apr 2025
p-ISSN: 2395-0072
www.irjet.net
AI and Reinforcement Learning in Robotics Suryansh Garg Undergraduate Student, Dept. of Science and Computing, Chandigarh Business School of Administration Landran, Mohali, Punjab, India ---------------------------------------------------------------------***---------------------------------------------------------------------
Abstract - A major development in artificial intelligence
Soft Actor-Critic (SAC). Analyzed were simulation results and empirical data in order to identify trends, challenges, and areas of future research direction. This methodological framework supports the objective of including present difficulties, acknowledging current knowledge, and suggesting future directions for the application of reinforcement learning in robotics.
(AI), reinforcement learning (RL) lets robots learn on their own by means of experience and error. The integration of artificial intelligence and reinforcement learning in robotics is investigated in this work together with their applications, related difficulties, and future possibilities. We show how RL improves robotic performance and adaptability by analyzing industrial automation, autonomous vehicles, and healthcare robotics. While suggesting future directions of research in this field, the paper also addresses important challenges including sample efficiency, the exploration-exploitation dilemma, and real-world adaptability.
3. Reinforcement Learning in Robotics
Key Words: Artificial Intelligence, Reinforcement Learning, Robotics, Robotic Performance, Industrial Automation, Sample Efficiency
In the machine learning paradigm known as reinforcement learning, an agent interacts with an environment and learns from feedback—that of rewards or penalties. By means of repeated interactions, this method helps robots to create ideal decision-making strategies, so enhancing their efficiency and effectiveness in difficult tasks.
1.INTRODUCTION
4. Applications
Design, construction, and programming of machines able to perform different tasks constitute robotics. Robots have traditionally been pre-programmed to follow set instructions, so restricting their capacity to change with the times. But thanks to artificial intelligence—especially Reinforcement Learning—robots can now learn from experience, hone their behavior and over time maximize their performance. Like human learning, RL helps robots to independently explore their surroundings, learn from rewards and penalties, and make decisions.
4.1 Industrial Automation RL improves robotic efficiency in manufacturing and logistics when handling jobs including quality control, sorting, packaging, and product scanning. For inventory control and operation simplification, for example, Amazon's warehouses use RL-powered robots. By always learning and improving their performance, these robots help to lower mistakes and raise output.
2. Methodology This paper investigates using a qualitative and exploratory research approach the integration of artificial intelligence, more especially reinforcement learning (RL), in robotic systems. The method calls for a careful reading of academic publications, business studies, and case studies showing the applications, benefits, and limitations of reinforcement learning in robotics. To assure a thorough and in-depth knowledge, sources were selected from eminent conferences and publications in the fields of machine learning, robotics, and artificial intelligence. Three main application areas— healthcare robotics, autonomous cars, and industrial automation—where reinforcement learning has clearly shown impact—were the focus of the review.
Figure-1: RL-Driven Robotic Arm in Industrial Automation
Moreover, a comparison of several reinforcement learning techniques and their performance in real-world robotics environments was done. These comprise methods including Q-learning, Policy Gradient, Deep Q-Networks (DQN),
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Figure- 1 : Showcase of a robotic arm based on reinforcement learning sorting objects in a manufacturing setting to raise efficiency and lower error rates.
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