18 accepted papers
Vision-language pre-training (VLP) models, trained on large-scale image-text pairs, have become widely used across a variety of downstream vision-and-language (V+L) tasks. This widespread adoption raises concerns about their vulnerability to adversarial attacks. Non-universal adversarial attacks, wh…
MorphoBall is a bio-inspired, deformable spherical robot designed for multimodal locomotion across terrestrial and aquatic environments. By integrating a dual-mode drive system (spherical rolling and differential-drive) with a morphology-mediated propulsion mechanism, MorphoBall achieves adaptive mo…
The rapid evolution of Large Language Models (LLMs) has accelerated the transition from conversational chatbots to general agents. However, effectively balancing empathetic communication with budget-aware decision-making remains an open challenge. Since existing methods fail to capture these complex…
People who stutter (PWS) face systemic exclusion in today’s voice-driven society, where access to voice assistants, authentication systems, and remote work tools increasingly depends on fluent speech. Current automatic speech recognition (ASR) systems, trained predominantly on fluent speech, fail to
Large language models (LLMs) are possessed of numerous beneficial capabilities, yet their potential inclination harbors unpredictable risks that may materialize in the future. We hence propose CRiskEval, a Chinese dataset meticulously designed for gauging the risk proclivities inherent in LLMs such…
The widespread deployment of large language models (LLMs) across various domains has made their safety a critical priority. Inspired by think-tank decision-making philosophy, we propose DiplomacyAgent, an LLM-based multi-agent system for diplomatic position analysis. With DiplomacyAgent, we are able
This paper presents a unified framework called MAC-Planner that combines Multi-Robot Task Allocation with Coverage Path Planning to better solve the online multi-robot coverage path planning (MCPP) problem. By dynamically assigning tasks and planning coverage paths based on the system's real-time co
With the increasing capability of large language models (LLMs), LLM-as-a-judge has emerged as a new evaluation paradigm. Compared with traditional automatic and manual evaluation, LLM evaluators exhibit better interpretability and efficiency. Despite this, existing LLM evaluators suffer from limited…
This article presents a nonlinear estimator on matrix Lie group that performs a fast unscented transformation with natural evolution of sigma points from a geometric perspective. Different from the existing methods, the proposed method preserves the original dynamic equations on the manifold, which
Multi-robot coverage planning has gained significant attention in recent years. In this letter, we introduce a novel approach called APF-CPP (Artificial Potential Field Based Multi-Robot Online Coverage Path Planning) to enhance the collaboration of multi-robot systems to accomplish coverage tasks i
Large language models (LLMs) have demonstrated prowess in a wide range of tasks. However, many LLMs exhibit significant performance discrepancies between high- and low-resource languages. To mitigate this challenge, we present FuxiTranyu, an open-source multilingual LLM, which is designed to satisfy…
The configuration of most robotic systems lies in continuous transformation groups. However, in mobile robot trajectory tracking, many recent works still naively utilize optimization methods for elements in vector space without considering the manifold constraint of the robot configuration. In this
Powered exoskeletons offer a promising solution for individuals who require assistance with daily activities. In addition to walking on flat terrain, the y are evolving to handle advanced scenarios such as stairs, allowing for a wider range of activities. To facilitate this advancement, an effective
The rapid development of Chinese large language models (LLMs) poses big challenges for efficient LLM evaluation. While current initiatives have introduced new benchmarks or evaluation platforms for assessing Chinese LLMs, many of these focus primarily on capabilities, usually overlooking potential a…
Inertial measurement units (IMUs), composed of gyroscopes, accelerometers, and magnetometers, have been widely used in the fields of human motion animation, rehabilitation, robotics, and aerospace. However, their performances degenerate remarkably with external acceleration and magnetic disturbance.
The REINFORCE algorithm \cite{williams1992simple} is popular in policy gradient (PG) for solving reinforcement learning (RL) problems. Meanwhile, the theoretical form of PG is from \cite{sutton1999policy}. Although both formulae prescribe PG, their precise connections are not yet illustrated. Recent…
IMU-based human joint motion acquisition system is attractive for real-time control and monitoring in the emerging wearable technology due to its portability. However, in practical applications, it heavily suffers from long-term drift, magnetic interference and inconsistency of rotational reference
Variable impedance control is advantageous for physical human-robot interaction to improve safety, adaptability and many other aspects. This paper presents a gain-scheduled variable stiffness control approach under strict frequency-domain constraints. Firstly, to reduce conservativeness, we characte…