Presentation Type
Oral/Paper Presentation
Abstract
Teaching robots through reinforcement learning (RL) has made great progress, but real-world training is still difficult. Robots need lots of practice to learn, rewards can be hard to define, and resetting the environment after each attempt is often a challenge. The Sample-Efficient Robotic Reinforcement Learning (SERL) framework helps solve these issues by offering a ready-to-use, open-source software package that makes RL more practical for real-world robotics.
This project explores SERL and how it improves robotic RL by making learning faster and more efficient. SERL includes smarter ways to reuse training data, automatic methods for understanding rewards from images, and a system that helps robots reset tasks on their own. It also provides strong robot controllers to handle tricky tasks that involve physical contact.
SERL has been tested on real robots and has successfully learned tasks like assembling circuit boards, organizing objects, and routing cables—all in less than an hour of training. This challenges the common belief that RL is too slow for real-world use. Because SERL is open source, it makes advanced robotic learning more accessible to researchers and engineers.
By reviewing SERL’s features and results, this work highlights how it makes RL more practical and useful for robotics.
Faculty Mentor
Dr. Juan Rojas, juan.rojas@lipscomb.edu
Recommended Citation
Jugovic, Faris; Ruiz-Martinez, Cleiver; and Stelt, Ryan Vander, "Making Robotic Reinforcement Learning More Efficient: Analyzing the SERL Framework" (2025). Student Scholar Symposium. 84.
https://digitalcollections.lipscomb.edu/student_scholars_symposium/2025/Full_schedule/84
Included in
Making Robotic Reinforcement Learning More Efficient: Analyzing the SERL Framework
Teaching robots through reinforcement learning (RL) has made great progress, but real-world training is still difficult. Robots need lots of practice to learn, rewards can be hard to define, and resetting the environment after each attempt is often a challenge. The Sample-Efficient Robotic Reinforcement Learning (SERL) framework helps solve these issues by offering a ready-to-use, open-source software package that makes RL more practical for real-world robotics.
This project explores SERL and how it improves robotic RL by making learning faster and more efficient. SERL includes smarter ways to reuse training data, automatic methods for understanding rewards from images, and a system that helps robots reset tasks on their own. It also provides strong robot controllers to handle tricky tasks that involve physical contact.
SERL has been tested on real robots and has successfully learned tasks like assembling circuit boards, organizing objects, and routing cables—all in less than an hour of training. This challenges the common belief that RL is too slow for real-world use. Because SERL is open source, it makes advanced robotic learning more accessible to researchers and engineers.
By reviewing SERL’s features and results, this work highlights how it makes RL more practical and useful for robotics.