In the field of autonomous vehicles (AVs), accurately discerning commander intent and
executing linguistic commands within a visual context presents a significant challenge. This …

Abstract Human-Agent Interaction is at the forefront of rapid development, with integrating
deep learning techniques into natural language processing representing significant …

We investigate the Vision-and-Language Navigation (VLN) problem in the context of
autonomous driving in outdoor settings. We solve the problem by explicitly grounding the …

Referring image segmentation aims to segment the instance corresponding to the given
language description, which requires aligning information from two modalities. Existing …

Advancements in vehicle automation technology are expected to significantly impact how
humans interact with vehicles. In this study, we propose a method to create user-friendly …

The perception of Deformable Linear Objects (DLOs) is a challenging task due to their
complex and ambiguous appearance, lack of discernible features, typically small sizes, and …

Traditional autonomous driving systems have mainly focused on making driving decisions
without human interaction, overlooking human-like decision-making and human preference …

In this study, we develop a model that enables mobilities to have more friendly interactions
with users. Specifically, we focus on the referring navigable regions task in which a model …

This paper presents the Language Aided Subset Sampling Based Motion Planner (LASMP),
a system that helps mobile robots plan their movements by using natural language …

Existing Vision-Language models (VLMs) estimate either long-term trajectory waypoints or a
set of control actions as a reactive solution for closed-loop planning based on their rich …