Reinforcement Learning in Robotics: Challenges and Applications
Main Article Content
Abstract
Reinforcement Learning (RL) is at the vanguard of robotics revolution, allowing machines to learn and make choices in complicated environments. This paper explores the symbiotic dating between RL and robotics, focusing at the challenges and programs that shape this interdisciplinary area. The dialogue makes a speciality of the fundamental standards of RL and its integration into robotics, elucidating the specific demanding situations encountered on this merger. The research covers the space among simulation-primarily based schooling and real-world applicability, navigating hardware obstacles, and addressing safety concerns, organising a comprehensive view of the challenges encountered when deploying RL for robotic systems. This paper also offers insights into the various programs of RL in robotics, inclusive of self sustaining navigation, item manipulation, healthcare, and business automation. It investigates how RL algorithms assist robots navigate complex environments, gain item manipulation dexterity, and contribute to healthcare improvements and industrial optimization. Case studies exhibit the software of RL in robotics by means of highlighting a hit implementations and demonstrating the transformative capability of this aggregate. Finally, this paper outlines future instructions, paving the way for persisted innovation and emphasizing the importance of bridging the theoretical advancements and real-international deployment in RL-driven robotics.
Downloads
Metrics
Article Details
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
You are free to:
- Share — copy and redistribute the material in any medium or format for any purpose, even commercially.
- Adapt — remix, transform, and build upon the material for any purpose, even commercially.
- The licensor cannot revoke these freedoms as long as you follow the license terms.
Under the following terms:
- Attribution — You must give appropriate credit , provide a link to the license, and indicate if changes were made . You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.
- No additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.
Notices:
You do not have to comply with the license for elements of the material in the public domain or where your use is permitted by an applicable exception or limitation .
No warranties are given. The license may not give you all of the permissions necessary for your intended use. For example, other rights such as publicity, privacy, or moral rights may limit how you use the material.
References
Sutton, R. S., & Barto, A. G. (2018). Reinforcement Learning: An Introduction (2nd ed.). MIT Press.
Kober, J., Bagnell, J. A., & Peters, J. (Eds.). (2013). Reinforcement Learning in Robotics: A Survey. Springer.
Lillicrap, T. P., et al. (2015). Continuous Control with Deep Reinforcement Learning. arXiv preprint
arXiv:1509.02971.
Levine, S., et al. (2016). End-to-End Training of Deep Visuomotor Policies. Journal of Machine Learning
Research, 17(39), 1-40.
Kalashnikov, D., et al. (2018). QT-Opt: Scalable Deep Reinforcement Learning for Vision-Based Robotic
Manipulation. arXiv preprint arXiv:1806.10293.
Pinto, L., et al. (2017). The Curious Robot: Learning Visual Representations via Physical Interactions. In
Proceedings of the IEEE International Conference on Robotics and Automation (ICRA).
R. K. Kaushik Anjali and D. Sharma, "Analyzing the Effect of Partial Shading on Performance of Grid
Connected Solar PV System", 2018 3rd International Conference and Workshops on Recent Advances and
Innovations in Engineering (ICRAIE), pp. 1-4, 2018.
Schulman, J., et al. (2015). Trust Region Policy Optimization. In Proceedings of the 32nd International
Conference on Machine Learning (ICML).
OpenAI. (2020). Solving Rubik's Cube with a Robot Hand. [Research paper]. OpenAI.
Rusu, A. A., et al. (2016). Sim-to-Real Robot Learning from Pixels with Progressive Nets. arXiv preprint
arXiv:1610.04286.
Zhang, T., et al. (2018). Fully Convolutional Hierarchical Attention Network for Text-Based Video
Segmentation. In Proceedings of the European Conference on Computer Vision (ECCV).
Pathak, D., et al. (2018). Zero-Shot Visual Imitation. arXiv preprint arXiv:1804.08619.
Zeng, A., et al. (2018). Learning Synergies between Pushing and Grasping with Self-supervised Deep
Reinforcement Learning. arXiv preprint arXiv:1803.09956.
Zoph, B., et al. (2018). Learning Transferable Architectures for Scalable Image Recognition. In Proceedings
of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
Amos, B., et al. (2018). Learning to Move in 3D Environments with Deep Reinforcement Learning. arXiv
preprint arXiv:1807.11158.