12 accepted papers
As the complexity of tasks addressed through reinforcement learning (RL) increases, the definition of reward functions also has become highly complicated. We introduce an RL method aimed at simplifying the reward-shaping process through intuitive strategies. Initially, instead of a single reward fun
With the increase of robots navigating through crowded environments in our daily lives, the demand for designing a socially-aware navigation method considering humanrobot interaction has risen. When developing and assessing socially-aware navigation methods, pedestrian motion modeling plays a signif…
In the realm of autonomous agents, ensuring safety and reliability in complex and dynamic environments remains a paramount challenge. Safe reinforcement learning addresses these concerns by introducing safety constraints, but still faces challenges in navigating intricate environments such as comple…
Satisfying safety constraints in reinforcement learning (RL) is an important issue, especially in real-world applications. Many studies have approached safe RL with the Lagrangian method, which introduces dual variables. However, applying a trained policy with the optimal dual variable to a new envi…
The structure of roads plays an important role in designing autonomous driving algorithms. We propose a novel road graph based driving framework, named RIANet++. The proposed framework considers the road structural scene context by incorporating both graphical features of the road and visual informa
Designing a socially-aware navigation method for crowded environments has become a critical issue in robotics. In order to perform navigation in a crowded environment without causing discomfort to nearby pedestrians, it is necessary to design a global planner that is able to consider both human-robo…
Reinforcement learning from human feedback (RLHF) alleviates the problem of designing a task-specific reward function in reinforcement learning by learning it from human preference. However, existing RLHF models are considered inefficient as they produce only a single preference data from each human…
In this paper, we present a novel autonomous driving framework, called a road graph and image attention network (RIANet), which computes the attention scores of objects in the image using the road graph feature. The process of the proposed method is as follows: First, the feature encoder module enco…
Designing or learning an autonomous driving policy is undoubtedly a challenging task as the policy has to maintain its safety in all corner cases. In order to secure safety in autonomous driving, the ability to detect hazardous situations, which can be seen as an out-of-distribution (OOD) detection…
We propose a novel autonomous driving frame-work that leverages graph-based features of roads, such as road positions and connections. The proposed method is divided into two parts: a low-level controller which follows the trajectory calculated by a graph-based path planner, and a high-level control…
In this paper, we consider autonomous driving of a vehicle using imitation learning. Generative adversarial imitation learning (GAIL) is a widely used algorithm for imitation learning. This algorithm leverages positive demonstrations to imitate the behavior of an expert. In this paper, we propose a…
Autonomous driving is a challenging problem because the autonomous vehicle must understand complex and dynamic environment. This understanding consists of predicting future behavior of nearby vehicles and recognizing predefined rules. It is observed that not all rules have equivalent values, and the…