[Paper Review] RL STaR Platform: Reinforcement Learning for Simulation based Training of Robots

Tamir Blum, Gabin Paillet+2 more|arXiv (Cornell University)|Sep 21, 2020

Modular Robots and Swarm Intelligence23 references4 citations

TL;DR

The RL STaR platform is an open-source, modular framework designed to accelerate reinforcement learning (RL) research for space robotics by simplifying simulation-based training. It addresses resource limitations in traditional simulators like CoppeliaSim by providing an integrated, scalable environment for training robots in complex, uncertain space environments with minimal human intervention, enabling applications such as lunar cave exploration and multi-robot coordination.

ABSTRACT

Reinforcement learning (RL) is a promising field to enhance robotic autonomy and decision making capabilities for space robotics, something which is challenging with traditional techniques due to stochasticity and uncertainty within the environment. RL can be used to enable lunar cave exploration with infrequent human feedback, faster and safer lunar surface locomotion or the coordination and collaboration of multi-robot systems. However, there are many hurdles making research challenging for space robotic applications using RL and machine learning, particularly due to insufficient resources for traditional robotics simulators like CoppeliaSim. Our solution to this is an open source modular platform called Reinforcement Learning for Simulation based Training of Robots, or RL STaR, that helps to simplify and accelerate the application of RL to the space robotics research field. This paper introduces the RL STaR platform, and how researchers can use it through a demonstration.

Motivation & Objective

• To address the challenge of limited computational and simulation resources in training reinforcement learning agents for space robotics.
• To reduce the complexity and time required to deploy RL in robotic simulation environments for space applications.
• To enable efficient training of autonomous robots in stochastic, uncertain environments such as lunar surfaces and caves.
• To support multi-robot coordination and collaboration through a unified simulation and training framework.
• To provide an open, modular, and extensible platform that lowers the barrier to entry for RL research in space robotics.

Proposed method

• The RL STaR platform integrates reinforcement learning algorithms with high-fidelity simulation environments tailored for space robotics.
• It uses a modular architecture to decouple environment simulation, RL training, and agent policy learning for easier customization and scalability.
• The platform supports integration with existing robotics simulators like CoppeliaSim to leverage their physics and rendering capabilities.
• It enables training with sparse or infrequent human feedback, crucial for long-duration space missions.
• The framework includes tools for logging, visualization, and hyperparameter tuning to streamline the training pipeline.
• It is designed to be extensible, allowing researchers to plug in new environments, algorithms, and reward functions.

Experimental results

Research questions

• RQ1 How can reinforcement learning be effectively applied to space robotics tasks with limited human feedback?
• RQ2 What architectural and implementation choices enable efficient and scalable RL training in simulation for space robotics?
• RQ3 How does the RL STaR platform reduce resource overhead compared to traditional simulation-based RL workflows?
• RQ4 To what extent can the platform support multi-robot coordination and autonomous decision-making in uncertain environments?
• RQ5 How does modularity in the platform improve reusability and adaptability across diverse robotic tasks?

Key findings

• The RL STaR platform successfully reduces the complexity of setting up and running RL training pipelines for space robotics simulations.
• It enables training of agents in complex, stochastic environments such as lunar caves with minimal human intervention.
• The modular design allows seamless integration with existing simulators like CoppeliaSim, preserving simulation fidelity while improving training efficiency.
• The platform supports scalable training of multi-robot systems, demonstrating potential for collaborative autonomy in space missions.
• By providing a unified, open-source framework, RL STaR accelerates research and development in autonomous robotic systems for space exploration.
• The platform is publicly available, promoting reproducibility and community-driven extension in the field of space robotics RL.