11 accepted papers
Large language models are trained with tokenizers that map text to a fixed vocabulary, yet the resulting token distribution is highly imbalanced: a few words dominate the stream while most occur rarely. Recent practice favours ever-larger vocabularies, but it is unclear whether the benefit comes fr…
Building a universal multilingual automatic speech recognition (ASR) model that performs equitably across languages has long been a challenge due to its inherent difficulties. To address this task we introduce a Language-Agnostic Multilingual ASR pipeline through orthography Unification and language…
The Bradley-Terry (BT) model is widely practiced in reward modeling for reinforcement learning with human feedback (RLHF). Despite its effectiveness, reward models (RMs) trained with BT model loss as one-way classifiers are prone to over-optimization, losing generalizability to unseen inputs. In thi…
Code-switching (CS), the alternation between two or more languages within a single speaker’s utterances, is common in real-world conversations and poses significant challenges for multilingual speech technology. However, systems capable of handling this phenomenon remain underexplored, primarily due
Mobile robots operating in outdoor environments face the challenge of navigating various terrains with different degrees of difficulty. Therefore, traversability estimation is crucial for safe and efficient robot navigation. Current approaches utilize a robot's driving experience to learn traversabi
Stable pre-training is essential for achieving better-performing language models. However, tracking pre-training stability is impractical due to high computational costs. We study Token Embedding Variability as a simple proxy to estimate pre-training stability. We theoretically and empirically demon…
This paper presents a method for LiDAR sensor-based traversability analysis for autonomous mobile robots in urban environments. Although urban environments are structured environments, a typical terrain comprises hazardous regions for mobile robots. Therefore, a reliable method for detecting travers…
Current self-driving cars can perform precise localization and generate collision-free trajectories using high definition (HD) maps which provide accurate road information. Therefore, keeping HD maps up to date is important for safe autonomous driving. In general, automotive HD maps are built by the
The article proposes a new heuristic for task allocation and routing of heterogeneous robots. Specifically, we consider a path planning problem where there are two (structurally) heterogeneous robots that start from distinctive depots and a set of targets to visit. The objective is to find a tour fo…
In this study, we propose a novel Light Detection and Ranging (LiDAR) sensor-based localization method for localization of a mobile robot. In localization using the LiDAR sensor, localization errors occur when real range measurements differ from reference distances computed from a map. This study fo…
Urban road environments that have pavement and curb are characterized as semi-structured road environments. In semi-structured road environments, the curb provides useful information for robot navigation. In this paper, we present a practical localization method for outdoor mobile robots using the c…