Building upon the foundations laid by our previous work, this paper introduces Arena 4.0, a significant advancement of Arena 3.0 [1], Arena-Bench [2], Arena 1.0 [3], and Arena 2.0 [4]. Arena 4.0 provides three main novel contributions: 1) a generative-model-based world and scenario generation approach using large language models (LLMs) and diffusion models, to dynamically generate complex, humancentric environments from text prompts or 2D floorplans that can be used for development and benchmarking of social navigation strategies. 2) A comprehensive 3D model database which can be extended with 3D assets and semantically linked and annotated using a variety of metrics for dynamic spawning and arrangements inside 3D worlds. 3) The complete migration towards ROS 2, which ensures operation with state-of-the-art hardware and functionalities for improved navigation, usability, and simplified transfer towards real robots. We evaluated the platforms performance through a comprehensive user study and its world generation capabilities for benchmarking demonstrating significant improvements in usability and efficiency compared to previous versions. Arena 4.0 is openly available at https://github.com/Arena-Rosnav.

Social navigation has become increasingly important for robots operating in human environments, yet many newly proposed navigation methods remain narrowly tailored or exist only as proof-of-concept prototypes. Building on our previous work with Arena, a social navigation development platform, we now propose, Arena-Bench 2.0 a comprehensive social navigation benchmark of state-of-the-art planners, fully integrated into the Arena framework. To achieve this, we developed a novel plugin structure—implemented on ROS2—to streamline the integration process and ensure straightforward, efficient workflows. As a demonstration, we integrated various learning-based and model-based navigation approaches and constructed a diverse set of social navigation scenarios to rigorously evaluate each planner. Specifically, we introduce a scenario generation node that allows users to construct complex, realistic social contexts through a web-based interface. We subsequently perform an extensive benchmark of all integrated planners, assessing both navigational and social metrics. Our evaluation also considers factors such as sensor input, reaction time, and latency, enabling insights into which planner may be most appropriate under different circumstances. The findings offer valuable guidance for selecting suitable planners for specific scenarios. The code is publicly available at https://github.com/Arena-Rosnav.

The ability of a robot to perceive and understand its environment is crucial for its actions and behavior. Humans are adept at using semantic information for object localization and path planning, a skill that robots need to emulate for intelligent adaptation in dynamic settings. Training of the spatial anticipation ability, which can enhance spatial perception through semantic understanding, necessitates the availability of appropriate data. Although extensive research has been conducted on datasets for outdoor environments, especially in the context of autonomous driving, there is still a notable lack of datasets specifically designed for indoor environments, with a focus on dynamic object localization. This paper introduces HabitatDyn 2.0, a dataset specifically designed for enhancing object localization capabilities with semantic information from a robot’s perspective. Besides RGB videos, semantic annotations, and depth information, HabitatDyn 2.0 also features top-down view labels for dynamic objects, which is required for training the spatial anticipation ability based on semantic information. Additionally, an algorithm that leverages spatial anticipation for dynamic object localization is presented, trained, and evaluated on the dataset.

Assistance robots have gained widespread attention in various industries such as logistics and human assistance. The tasks of guiding or following a human in a crowded environment such as airports or train stations to carry weight or goods is still an open problem. In these use cases, the robot is not only required to intelligently interact with humans, but also to navigate safely among crowds. Thus, especially highly dynamic environments pose a grand challenge due to the volatile behavior patterns and unpredictable movements of humans. In this paper, we propose a Deep-Reinforcement-Learning-based agent for human-guiding and -following tasks in crowded environments. Therefore, we incorporate semantic information to provide the agent with high-level information like the social states of humans, safety models, and class types. We evaluate our proposed approach against a benchmark approach without semantic information and demonstrated enhanced navigational safety and robustness. Moreover, we demonstrate that the agent could learn to adapt its behavior to humans, which improves the human-robot interaction significantly.

Recently, mobile robots have become important tools in various industries, especially in logistics. Deep reinforcement learning emerged as an alternative planning method to replace overly conservative approaches and promises more efficient and flexible navigation. However, deep reinforcement learning approaches are not suitable for long-range navigation due to their proneness to local minima and lack of long term memory, which hinders its widespread integration into industrial applications of mobile robotics. In this paper, we propose a navigation system incorporating deep-reinforcement-learning- based local planners into conventional navigation stacks for long-range navigation. Therefore, a framework for training and testing the deep reinforcement learning algorithms along with classic approaches is presented. We evaluated our deep-reinforcement-learning-enhanced navigation system against various conventional planners and found that our system outperforms them in terms of safety, efficiency and robustness.

Deep Reinforcement Learning has emerged as an efficient dynamic obstacle avoidance method in highly dynamic environments. It has the potential to replace overly conservative or inefficient navigation approaches. However, integrating Deep Reinforcement Learning into existing navigation systems is still an open frontier due to the myopic nature of Deep-Reinforcement-Learning-based navigation, which hinders its widespread integration into current navigation systems. In this paper, we propose the concept of an intermediate planner to interconnect novel Deep-Reinforcement-Learning-based obstacle avoidance with conventional global planning methods using waypoint generation. Therefore, we integrate different waypoint generators into existing navigation systems and compare the joint system against traditional ones. We found an increased performance in terms of safety, efficiency and path smoothness, especially in highly dynamic environments.

The imagination of the surrounding environment based on the experience and semantic cognition has great potential to extend the limited observations to leverage the ability for mapping, collision avoidance and path planning. This paper provides a training-based algorithm for mobile robots to perform spatial imagination based on semantic cognition and evaluates the proposed method for the mapping task. We utilize a photo-realistic simulation environment, Habitat, for training and evaluation. The trained model is composed of Resent-18 as encoder and U-net as the backbone. We demonstrate that the algorithm can perform imagination for unseen parts of the object universally, by recalling the images and experience and compare our approach with traditional semantic mapping methods. It is found that our approach will improve the efficiency and accuracy of semantic mapping.