Reinforcement Learning Techniques for Autonomous Robotics

Christopher Ryan Thompson; Rajasekhar Reddy Talla; Jaya Chandra Srikanth Gummadi; Arjun Kamisetty

Authors

Christopher Ryan Thompson Robotic Process Automation (RPA) Developer, American Robotics, Waltham, MA 02452, USA
Rajasekhar Reddy Talla SAP GTS Senior Analyst, Archer Daniels Midland (ADM), 1260 Pacific Ave, Erlanger, KY 41018, USA
Jaya Chandra Srikanth Gummadi Senior Software Engineer, Lowes Companies Inc., Charlotte, North Carolina, USA
Arjun Kamisetty Software Developer, Fannie Mae, 2000 Opportunity Wy, Reston, VA 20190, USA

Keywords:

Reinforcement Learning

Autonomous Robotics

Machine Learning

Robot Control

Deep Learning

Reinforcement Algorithms

Robotics Navigation

Intelligent Agents

Abstract

This paper examines reinforcement learning (RL) methods for autonomous robots and their strengths, weaknesses, and applications. The main goals are to assess sophisticated RL algorithms in robotics, identify problems, and suggest improvements. This secondary data-based review synthesizes current research on Deep Q-networks (DQN), policy gradient techniques, model-based approaches, and hierarchical RL. These strategies improve robotic learning by boosting sample efficiency, managing continuous actions, and enhancing real-time performance. Still, they also confront sim-to-real gaps, safety issues, and high computing demands. The paper recommends investing in simulation-to-reality transfer research, safety measures, and computational tools to solve these constraints. The study emphasizes the revolutionary potential of RL in autonomous robots and the need for continuing innovation and supporting policy to overcome limitations and fully harness RL capabilities in practical applications.

References

Bhagat, S., Banerjee, H., Tse, Z. T. H., Ren, H. (2019). Deep Reinforcement Learning for Soft, Flexible Robots: Brief Review with Impending Challenges. Robotics, 8(1), 4. https://doi.org/10.3390/robotics8010004

Da Silva, F. L., Reali Costa, A. H. (2019). A Survey on Transfer Learning for Multiagent Reinforcement Learning Systems. The Journal of Artificial Intelligence Research, 64, 645-703. https://doi.org/10.1613/jair.1.11396

Huo, Y., Li, Y., Feng, X. (2018). Model-Free Recurrent Reinforcement Learning for AUV Horizontal Control. IOP Conference Series. Materials Science and Engineering, 428(1). https://doi.org/10.1088/1757-899X/428/1/012063

Karanam, R. K., Natakam, V. M., Boinapalli, N. R., Sridharlakshmi, N. R. B., Allam, A. R., Gade, P. K., Venkata, S. G. N., Kommineni, H. P., & Manikyala, A. (2018). Neural Networks in Algorithmic Trading for Financial Markets. Asian Accounting and Auditing Advancement, 9(1), 115–126. https://4ajournal.com/article/view/95

Kormushev, P., Calinon, S., Caldwell, D. G. (2013). Reinforcement Learning in Robotics: Applications and Real-World Challenges [dagger]. Robotics, 2(3), 122-148. https://doi.org/10.3390/robotics2030122

Mohammed, M. A., Kothapalli, K. R. V., Mohammed, R., Pasam, P., Sachani, D. K., & Richardson, N. (2017a). Machine Learning-Based Real-Time Fraud Detection in Financial Transactions. Asian Accounting and Auditing Advancement, 8(1), 67–76. https://4ajournal.com/article/view/93

Mohammed, M. A., Mohammed, R., Pasam, P., & Addimulam, S. (2018). Robot-Assisted Quality Control in the United States Rubber Industry: Challenges and Opportunities. ABC Journal of Advanced Research, 7(2), 151-162. https://doi.org/10.18034/abcjar.v7i2.755

Mohammed, R., Addimulam, S., Mohammed, M. A., Karanam, R. K., Maddula, S. S., Pasam, P., & Natakam, V. M. (2017b). Optimizing Web Performance: Front End Development Strategies for the Aviation Sector. International Journal of Reciprocal Symmetry and Theoretical Physics, 4, 38-45. https://upright.pub/index.php/ijrstp/article/view/142

Pathak, S., Pulina, L., Tacchella, A. (2018). Verification and Repair of Control Policies for Safe Reinforcement Learning. Applied Intelligence, 48(4), 886-908. https://doi.org/10.1007/s10489-017-0999-8

Polydoros, A. S., Nalpantidis, L. (2017). Survey of Model-Based Reinforcement Learning: Applications on Robotics. Journal of Intelligent & Robotic Systems, 86(2), 153-173. https://doi.org/10.1007/s10846-017-0468-y

Raslan, H., Schwartz, H., Givigi, S. (2016). A Learning Invader for the "Guarding a Territory" Game: A Reinforcement Learning Problem. Journal of Intelligent & Robotic Systems, 83(1), 55-70. https://doi.org/10.1007/s10846-015-0317-9

Rodriguez-Ramos, A., Sampedro, C., Bavle, H., de la Puente, P., Campoy, P. (2019). A Deep Reinforcement Learning Strategy for UAV Autonomous Landing on a Moving Platform. Journal of Intelligent & Robotic Systems, 93(1-2), 351-366. https://doi.org/10.1007/s10846-018-0891-8

Sampedro, C., Rodriguez-Ramos, A., Bavle, H., Carrio, A., de la Puente, P. (2019). A Fully-Autonomous Aerial Robot for Search and Rescue Applications in Indoor Environments using Learning-Based Techniques. Journal of Intelligent & Robotic Systems, 95(2), 601-627. https://doi.org/10.1007/s10846-018-0898-1

Ying, D., Kothapalli, K. R. V., Mohammed, M. A., Mohammed, R., & Pasam, P. (2018). Building Secure and Scalable Applications on Azure Cloud: Design Principles and Architectures. Technology & Management Review, 3, 63-76. https://upright.pub/index.php/tmr/article/view/149

Zhifei, S., Er, M. J. (2012). A Survey of Inverse Reinforcement Learning Techniques. International Journal of Intelligent Computing and Cybernetics, 5(3), 293-311. https://doi.org/10.1108/17563781211255862