To handle navigation tasks, the popular Simultaneous Localization and Mapping (SLAM) and its variants [4; 5] can use sensors to generate the obstacle maps of the navigation environment. Planning algorithms (e.g., rapid-exploration random tree [6]) can then be applied to find the collision-free paths. However, building an accurate map can be expensive due to the need for sensor fusion [7], and map accuracy directly affects the quality of generated paths. Our work focuses on finding solutions for navigation in unknown environments without relying on maps. Traditional navigation methods, which depend on precise global maps, are often impractical in such environments. Therefore, we aim to develop methods that allow robots to navigate effectively without needing maps. We believe robots can learn navigation policies using reinforcement learning (RL), a technique that has been successful in fields like gaming [8; 9] and robotic manipulation [10; 11]. In RL, an agent learns to make decisions based on observations, with the goal of maximizing long-term rewards. Recently, RL has also been applied to navigation tasks in robotics [12; 13]. Although RL has shown promising results in navigation tasks, a major challenge is that the optimal policy must be learned through extensive interactions with the environment. It often takes a long time for a single agent to converge to the optimal solution, and it is nearly impossible for one agent to explore all possible states in real-world robotic tasks. To address this, distributed and parallel RL algorithms [14; 15] offer a potential solution. However, these methods require a central server to collect data from multiple agents for model training, raising concerns about information leakage and privacy [16]. Moreover, standard episodic RL algorithms often struggle with out-of-distribution observations and the adaptability of unfamiliar environments. For example, UGVs may face unfamiliar situations in warehouses, but novel obstacles may differ from prior learned experience. These challenges highlight the need for novel RL algorithms that can address these issues and enable effective navigation in unknown environments. This work employs the idea of federated learning (FL), which is a distributed machine learning technique that allows multiple agents to be trained simultaneously [17]. In FL systems, each agent can train its local model separately and send back the model-related parameters to an aggregation center. Each agent's local model is obtained based on its own observations, but it can be aggregated so as to construct a shared model. Most importantly, each agent only needs to process its own collected raw data, and send the parameters of its local model to the aggregation center. In this paper, we propose an FL-based reinforcement learning approach for the mapless navigation problem of UGVs for unfamiliar environments. In particular, we address how the navigation capabilities of UGVs can be learned based on sensor-level observations, how multiple agents can be used to speed up the learning procedures, and how knowledge aggregation can be developed to ensure the generalization of new environments and obstacles  
Figure 1: UGVs may encounter unfamiliar obstacles with various sizes and shapes in a smart factory. never seen before. To this end, the performance of each learning round will be evaluated by the growth rate of each agent's local model, and the knowledge aggregation mechanism will be achieved based on such an evaluation. We also take account of the elimination of the interference of low-quality local models during the knowledge aggregation process. The main contributions of this work are summarized as follows. 1. We propose a federated growing reinforcement learning (FGRL) approach that allows multiple agents to be trained simultaneously so as to speed up the learning efficiency. 2. The proposed FGRL approach provides a sensor-level navigation and collision avoidance mechanism that does not require a precise global environmental map. 3.