13 accepted papers
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
In order to enhance driving convenience and safety, automotive Augmented Reality displays, e.g., head-up displays, have garnered attention and are gradually being deployed. However, when vehicles encounter uneven roads, vertical vibrations lead to mismatches between external physical objects and aug…
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
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
The dynamics of all real quadrotors inevitably differ even if they are the same product. In particular, the dynamics can change significantly during the flight due to additional device attachments or overheating motors. In this study, we focus on training a low-level controller, which operates in re…
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…
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
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…
Double-sided crawling robots have potential to be quite useful for real-world applications. For example, double-sided crawling robots keep crawling even when they are turned over. In this letter, we present a lightweight and compact crawling robot that can run on both sides. To make this possible, t