Search Results

Hierarchical reinforcement learning (HRL) incorporates temporal abstraction into reinforcement learning (RL) by explicitly taking advantage of hierarchical structures. Modern HRL typically designs a hierarchical agent composed of a high-level policy and ...

Robotic agents trained using reinforcement learning have the problem of taking unreliable actions in an out-of-distribution (OOD) state. Agents can easily become OOD in real-world environments because it is almost impossible for them to visit and learn ...

Although contrastive learning methods have shown prevailing performance on a variety of representation learning tasks, they encounter difficulty when the training dataset is long-tailed. Many researchers have combined contrastive learning and a logit ...

Learning shared structures across changing environments enables an agent to efficiently retain obtained knowledge and transfer it between environments. A skill is a promising concept to represent shared structures. Several recent works proposed ...

Locality from bounded receptive fields is one of the biggest problems that needs to be solved in convolutional neural networks. Meanwhile, operating convolutions in frequency domain provides complementary viewpoint to this dilemma, as a point-wise ...

3D LiDAR is widely used in autonomous systems such as self-driving cars and autonomous robots because it provides accurate 3D point clouds of the surrounding environment under harsh conditions. However, a high-resolution LiDAR is expensive and bulky. ...

Many robotic tasks are composed of a lot of temporally correlated sub-tasks in a highly complex environment. It is important to discover situational intentions and proper actions by deliberating on temporal abstractions to solve problems effectively. To ...

A highly accurate and robust real-time localization process is crucial for autonomous driving applications. Numerous methods for localization have been proposed, which combine various kinds of input, such as data from environmental sensors, inertial ...

Autonomous driving with imitation learning is vulnerable to the quality of an expert dataset. Typical driving involves situations or online data that are biased toward specific scenarios such as lane following or stop. This property causes an imbalance in ...

Autonomous vehicles must be able to understand the surrounding traffic flows and predict the future traffic conditions for planning a safe maneuver. During prediction, the action of autonomous vehicles should be considered, as it influences the ...

Curb detection is an essential function of autonomous vehicles in urban areas. However, curbs are difficult to detect in complex urban environments in which many dynamic objects exist. Additionally, curbs appear in a variety of shapes and sizes. Previous ...

How to explore environments is one of the most critical factors for the performance of an agent in reinforcement learning. Conventional exploration strategies such as ε-greedy algorithm and Gaussian exploration noise simply depend on pure ...

Most real-world tasks are compound tasks that consist of multiple simpler sub-tasks. The main challenge of learning compound tasks is that we have no explicit supervision to learn the hierarchical structure of compound tasks. To address this challenge, ...

Highly accurate and robust real-time localization is an essential technique for various autonomous driving applications. Numerous localization methods have been proposed that combine various types of sensors, including an environmental sensor, IMU and ...

While object tracking for 3D point clouds has been widely researched in recent years, most trackers employ a direct point-to-point matching method under the assumption that target object clouds are dense, although the method is not suitable for sparse ...

In this paper, we present a driverless taxi system for autonomous campus mobility services. College campuses have unique mobility requirements in terms of layout, population, and demand and patterns. It is typically recommended to minimize the presence ...

In LIght Detection And Ranging (LIDAR)-based object detection, accurate object segmentation is of great importance, since segmentation is an essential preprocessing step for other perception tasks, such as classification and tracking. For segmenting ...

In driving environments, other vehicles are one of the most frequently appearing close range objects from the ego-vehicle. Thus, the development of high accuracy vehicle recognition algorithms is essential for safe and efficient automated driving. However,...

Over the last decade, automated vehicles have been widely researched and their massive potential has been verified through several milestone demonstrations. However, there are still many challenges ahead. One of the biggest challenges is integrating them ...

As the around-view monitoring (AVM) system becomes one of the essential components for advanced driver assistance systems (ADAS), many applications using AVM such as parking guidance system are actively being developed. As a key step for such applications,...