JaxRobotarium: Training and Deploying Multi-Robot Policies in 10 Minutes (2505.06771v2)

Abstract: Multi-agent reinforcement learning (MARL) has emerged as a promising solution for learning complex and scalable coordination behaviors in multi-robot systems. However, established MARL platforms (e.g., SMAC and MPE) lack robotics relevance and hardware deployment, leaving multi-robot learning researchers to develop bespoke environments and hardware testbeds dedicated to the development and evaluation of their individual contributions. The Multi-Agent RL Benchmark and Learning Environment for the Robotarium (MARBLER) is an exciting recent step in providing a standardized robotics-relevant platform for MARL, by bridging the Robotarium testbed with existing MARL software infrastructure. However, MARBLER lacks support for parallelization and GPU/TPU execution, making the platform prohibitively slow compared to modern MARL environments and hindering adoption. We contribute JaxRobotarium, a Jax-powered end-to-end simulation, learning, deployment, and benchmarking platform for the Robotarium. JaxRobotarium enables rapid training and deployment of multi-robot RL (MRRL) policies with realistic robot dynamics and safety constraints, supporting parallelization and hardware acceleration. Our generalizable learning interface integrates easily with SOTA MARL libraries (e.g., JaxMARL). In addition, JaxRobotarium includes eight standardized coordination scenarios, including four novel scenarios that bring established MARL benchmark tasks (e.g., RWARE and Level-Based Foraging) to a robotics setting. We demonstrate that JaxRobotarium retains high simulation fidelity while achieving dramatic speedups over baseline (20x in training and 150x in simulation), and provides an open-access sim-to-real evaluation pipeline through the Robotarium testbed, accelerating and democratizing access to multi-robot learning research and evaluation. Our code is available at https://github.com/GT-STAR-Lab/JaxRobotarium.

• The paper introduces JaxRobotarium, a Jax-based framework for multi-robot reinforcement learning that significantly accelerates training and simulation speeds (up to 20x and 150x respectively) compared to prior methods.
• JaxRobotarium provides realistic robotics simulation using control barrier certificates for collision avoidance, standardizes eight multi-robot coordination scenarios, and integrates seamlessly with existing MARL libraries.
• Empirical evaluation demonstrates JaxRobotarium's efficiency and sim-to-real performance via the Robotarium platform, setting a new standard for evaluating MRRL algorithms on tasks mimicking real-world conditions.

JaxRobotarium: Accelerating Multi-Robot Policy Training and Deployment

The paper "JaxRobotarium: Training and Deploying Multi-Robot Policies in 10 Minutes" presents a sophisticated framework, JaxRobotarium, designed to address existing limitations in multi-agent reinforcement learning (MARL) platforms for multi-robot systems. Traditional environments like SMAC and MPE are insufficient for robotics contexts due to their abstraction from real hardware dynamics, leading to a significant simulation-to-reality (sim-to-real) gap. The JaxRobotarium framework, developed at Georgia Institute of Technology, offers a Jax-based solution for rapid training and deployment of multi-robot reinforcement learning (MRRL) policies, enhancing both computational efficiency and realism.

Key Contributions of JaxRobotarium

1. Efficient Simulation and Training: JaxRobotarium is engineered using Jax, a high-performance library, to parallelize simulations and leverage GPU/TPU acceleration. This boosts training speeds by up to 20 times and simulation speeds by 150 times compared to MARBLER, the previous benchmark framework.
2. Realistic Robotics Simulation: The platform incorporates realistic robot dynamics managed through control barrier certificates for collision avoidance, facilitating a closer alignment between simulated training environments and physical deployments.
3. Standardized Scenario Set: Beyond merely improving a platform's execution speed, JaxRobotarium offers eight standardized coordination scenarios, with four drawing from existing MARL benchmark tasks like RWARE and Level-Based Foraging, adapted for real-world robotics settings.
4. Comprehensive Interface: JaxRobotarium provides an interface that integrates with state-of-the-art MARL libraries, such as JaxMARL, enabling seamless adaptation and minimal configuration adjustments for various algorithmic implementations.

Empirical Evaluation and Implications

The paper is thorough in its empirical evaluation, demonstrating the significant benefits of JaxRobotarium through efficiency benchmarks and comparative analysis against MARBLER. The paper showcases its utility in accelerating training by leveraging parallel environments and just-in-time compilation, significantly bridging the existing gap in robot policy training. By achieving remarkable simulation fidelity at high speeds, JaxRobotarium sets a new standard for evaluating MRRL algorithms on tasks that closely mimic real-world conditions.

Sim-to-Real Performance

JaxRobotarium includes a robust sim-to-real evaluation pipeline via the Robotarium, allowing researchers to deploy their trained policies in physical robot testbeds and analyze performance discrepancies. While JaxRobotarium addresses many sim-to-real challenges, the authors recognize that specific task characteristics and algorithm designs can still exhibit performance gaps. Through simple domain randomization techniques, notably action noise, some of these differences between simulated and real environments can be mitigated, illustrating critical pathways in reducing barriers between simulation and practical deployment.

Future Directions and Challenges

While the paper makes substantial advancements in the MRRL domain, it highlights limitations due to the fixed dynamics of the Robotarium's GRITSBots and the challenges of simulating complex perception modules. Future improvements could involve accommodating more intricate sensing elements like LiDAR or image data, which are pivotal for certain applications of robotic systems. The research community can leverage JaxRobotarium as a foundational tool to further explore these areas and develop more robust multi-robot policies with decreased sim-to-real gaps.

In summary, the introduction of JaxRobotarium represents a major step forward in the development and evaluation of MRRL algorithms. Its design focuses on speed, fidelity, and accessibility, offering a valuable resource for researchers looking to advance the state-of-the-art in robotic coordination and learning. Through standardized scenarios and a seamless integration with existing MARL libraries, JaxRobotarium not only accelerates research processes but also democratizes the field by providing an open-access platform for global use.