NavBench: A Unified Robotics Benchmark for Reinforcement Learning-Based Autonomous Navigation

Autonomous robots must navigate and operate in diverse environments, from terrestrial and aquatic settings to aerial and space domains. While Reinforcement Learning (RL) has shown promise in training policies for specific autonomous robots, existing benchmarks are often constrained to unique platforms, limiting generalization and fair comparisons across different mobility systems. In this paper, we present NavBench, a multi-domain benchmark for training and evaluating RL-based navigation policies across diverse robotic platforms and operational environments. Built on IsaacLab, our framework standardizes task definitions, enabling different robots to tackle various navigation challenges without the need for ad-hoc task redesigns or custom evaluation metrics. Our benchmark addresses three key challenges: (1) Unified cross-medium benchmarking, enabling direct evaluation of diverse actuation methods (thrusters, wheels, water-based propulsion) in realistic environments; (2) Scalable and modular design, facilitating seamless robot-task interchangeability and reproducible training pipelines; and (3) Robust sim-to-real validation, demonstrated through successful policy transfer to multiple real-world robots, including a satellite robotic simulator, an unmanned surface vessel, and a wheeled ground vehicle. By ensuring consistency between simulation and real-world deployment, NavBench simplifies the development of adaptable RL-based navigation strategies. Its modular design allows researchers to easily integrate custom robots and tasks by following the framework's predefined templates, making it accessible for a wide range of applications. Our code is publicly available at NavBench.

Evaluating Robustness of Deep Reinforcement Learning for Autonomous Surface Vehicle Control in Field Tests

Despite significant advancements in Deep Reinforcement Learning (DRL) for Autonomous Surface Vehicles (ASVs), their robustness in real-world conditions, particularly under external disturbances, remains insufficiently explored. In this paper, we evaluate the resilience of a DRL-based agent designed to capture floating waste under various perturbations. We train the agent using domain randomization and evaluate its performance in real-world field tests, assessing its ability to handle unexpected disturbances such as asymmetric drag and an off-center payload. We assess the agent's performance under these perturbations in both simulation and real-world experiments, quantifying performance degradation and benchmarking it against an MPC baseline. Results indicate that the DRL agent performs reliably despite significant disturbances. Along with the open-source release of our implementation, we provide insights into effective training strategies, real-world challenges, and practical considerations for deploying DRLbased ASV controllers.