Here is the abstract of the work, directly in the words of the authors:

This paper presents a vision-based control framework for the autonomous navigation of wheeled mobile robots in city-like environments, including both straight roads and turns. The approach leverages Computer Vision techniques and OpenCV to extract lane line features and utilizes a previously established control law to compute the necessary steering commands. 

The proposed method enables the robot to accurately follow the lanes and seamlessly handle complex maneuvers such as consecutive turns. The framework has been rigorously validated through extensive simulations and real-world experiments using physical robots equipped with the ROS framework. Experimental evaluations were conducted at the DIAG Robotics Lab at Sapienza University of Rome, Italy, demonstrating the practicality of the proposed solution in realistic settings. 

This work bridges the gap between theoretical control strategies and their practical application, offering insights into vision-based navigation systems for autonomous robotics. A video demonstration of the experiments is available at https://youtu.be/tDvpwSj8X28.

Here is the abstract of the work, directly in the words of the authors:

As the cost of labelling and collecting real world data remains an issue for companies, simulator training and transfer learning slowly evolved to be the foundation of many state-of the-art projects. In this paper these methods are applied in the Duckietown setup where self-driving agents can be developed and tested.

Our aim was to train a selected artificial neural network for right lane segmentation on simulator generated stream of images as a comparison baseline, then use domain adaptation to be more precise and stable in the real environment. We have tested and compared four knowledge transfer methods that included domain transformation using CycleGAN and semi-supervised domain adaptation via Minimax Entropy.

As the latter was previously untested in semantic segmentation according to our best knowledge, we have contributed to showing it is indeed possible and produces promising results. Finally we have shown that it could also create a model that fulfills our performance requirements of stability and accuracy.We show that the selected methods are equally eligible for the simulation to real transfer learning problem, and that the simplest method delivers the best performance.

Here is the abstract of the work, directly in the words of the authors:

Deep reinforcement learning (RL) has recently led to many breakthroughs on a range of complex control tasks. However, the agent’s decision-making process is generally not transparent. The lack of interpretability hinders the applicability of RL in safety-critical scenarios. While several methods have attempted to interpret vision-based RL, most come without detailed explanation for the agent’s behavior. In this paper, we propose a self-supervised interpretable framework, which can discover interpretable features to enable easy understanding of RL agents even for non-experts. Specifically, a self-supervised interpretable network (SSINet) is employed to produce fine-grained attention masks for highlighting task-relevant information, which constitutes most evidence for the agent’s decisions. We verify and evaluate our method on several Atari 2600 games as well as Duckietown, which is a challenging self-driving car simulator environment. The results show that our method renders empirical evidences about how the agent makes decisions and why the agent performs well or badly, especially when transferred to novel scenes. Overall, our method provides valuable insight into the internal decision-making process of vision-based RL. In addition, our method does not use any external labelled data, and thus demonstrates the possibility to learn high-quality mask through a self-supervised manner, which may shed light on new paradigms for label-free vision learning such as self-supervised segmentation and detection.