34 accepted papers
Offline goal-conditioned reinforcement learning remains challenging for long-horizon tasks. While hierarchical approaches mitigate this issue by decomposing tasks, most existing methods rely on separate high- and low-level networks and generate only a single intermediate subgoal, making them inadequ…
Recent SLAM systems have adopted 3D Gaussian Splatting (3DGS) to generate photorealistic maps. Despite its high fidelity, scaling 3DGS-SLAM to large scenes remains challenging. In most implementations, 3DGS maintains a single globally shared set of Gaussians, which can cause catastrophic forgetting
Semantic segmentation is crucial for autonomous navigation in off-road environments, enabling precise classification of surroundings to identify traversable regions. However, distinctive factors inherent to off-road conditions, such as source-target domain discrepancies and sensor corruption from ro…
3D Gaussian Splatting (3DGS) has recently emerged as a powerful technique for representing 3D scenes. Its superior high-fidelity rendering quality and speed have driven its rapid adoption in many applications. Among them, Visual Simultaneous Localization and Mapping (VSLAM) is the most prominent app…
End-to-end autonomous driving systems have demonstrated advantages over traditional modular systems. Despite this progress, these end-to-end systems still struggle to be deployed in real-world driving environments, as they inevitably encounter undertrained scenarios in which autonomous vehicles may
Reinforcement learning (RL) has made significant progress in various domains, but scaling it to long-horizon tasks with complex decision-making remains challenging. Skill learning attempts to address this by abstracting actions into higher-level behaviors. However, current approaches often fail to r…
Large language models (LLMs) have shown significant potential in guiding embodied agents to execute language instructions across a range of tasks, including robotic manipulation and navigation. However, existing methods are primarily designed for static environments and do not leverage the agent's o
Robots operating in human-centric or hazardous environments must proactively anticipate and mitigate dangers beyond basic obstacle detection. Traditional navigation systems often depend on static maps, which struggle to account for dynamic risks, such as a person emerging from a suddenly opening doo
Traversability mapping for autonomous navigation in unstructured environments has been widely investigated for decades. However, it remains challenging due to the uncertainty in geometry perception and the simplified representation of traversability maps that fail to capture detailed structures of e
The presence of Non-Line-of-Sight (NLoS) blind spots resulting from roadside parking in urban environments poses a significant challenge to road safety, particularly due to the sudden emergence of pedestrians. mmWave technology leverages diffraction and reflection to observe NLoS regions, and recent
3D Gaussian Splatting (3DGS) has recently emerged as a powerful technique for representing 3D scenes. Its superior high-fidelity rendering quality and speed have driven its rapid adoption in many applications. Among them, Visual Simultaneous Localization and Mapping (VSLAM) is the most prominent app
Traversability estimation is a core function for robot navigation in off-road unstructured environments and diverse research results have been published so far. One of the recent approaches is using the self-supervised learning (SSL) technique. SSL has been focused on as a breakthrough technique for
Precise and real-time localization is crucial for autonomous vehicles. State-of-the-art methods utilize 3D light detection and ranging (LiDAR), inertial measurement unit (IMU), and global positioning system (GPS). However, to meet real-time constraints, these methods often limit the search space to
In this study, we address the off-road traversability estimation problem, that predicts areas where a robot can navigate in off-road environments. An off-road environment is an unstructured environment comprising a combination of traversable and non-traversable spaces, which presents a challenge for…
A significant bottleneck in applying current reinforcement learning algorithms to real-world scenarios is the need to reset the environment between every episode. This reset process demands substantial human intervention, making it difficult for the agent to learn continuously and autonomously. Seve
Autonomous navigation in extreme mountainous terrains poses challenges due to the presence of mobility-stressing elements and undulating surfaces, making it particularly difficult compared to conventional off-road driving scenarios. In such environments, estimating traversability solely based on ext
Applying reinforcement learning to autonomous driving entails particular challenges, primarily due to dynamically changing traffic flows. To address such challenges, it is necessary to quickly determine response strategies to the changing intentions of surrounding vehicles. This letter proposes a ne
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 adjustment technique to address this problem, but…
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 the entire state space during training. Unfortu…
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 unsupervised skill discovery algorithms that can discov…
We propose a point-cloud-centric depth completion method called attention bilateral convolutional network for depth completion (ABCD). The proposed method uses LiDAR data and camera data to improve the resolution of the sparse depth information. Color images, which have been seen as fundamental to d
We present an accurate and robust image-to-point cloud registration method that is viable in urban and off-road environments. Existing image-to-point cloud registration methods have focused on vehicle platforms along paved roads. Therefore, image-to-point cloud registration on UGV platforms for off-
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. Although a low-resolution LiDAR is compact and af…
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 understand the intention separated from changi…
Deep reinforcement learning has been used to solve complex tasks in various fields, particularly in robotics control. However, agents trained using deep reinforcement learning have a problem of taking overconfident actions, even when the input state is far from the learned state distribution. This r
Retrosynthesis is the task of predicting reactant molecules from a given product molecule and is, important in organic chemistry because the identification of a synthetic path is as demanding as the discovery of new chemical compounds. Recently, the retrosynthesis task has been solved automatically…
Retrosynthesis, of which the goal is to find a set of reactants for synthesizing a target product, is an emerging research area of deep learning. While the existing approaches have shown promising results, they currently lack the ability to consider availability (e.g., stability or purchasability) o…
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 interaction between vehicles sharing the same traffic s…
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 the driving dataset, and it is highly likely…
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 studies have been based on the traditional pi…
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 randomness. However, it is required for an agent to consider…
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, previous imitation learning methods exploit tas…
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 point clouds. In this paper, we introduce a nove…
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 into urban environments in which dilemmas occ…