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In a rapidly evolving digital landscape autonomous tools and robots are becoming commonplace. Recognizing the significance of this development, this paper explores the integration of Large Language Models (LLMs) like Generative pre-trained…

人机交互 · 计算机科学 2024-03-22 Younes Lakhnati , Max Pascher , Jens Gerken

In autonomous exploration tasks, robots are required to explore and map unknown environments while efficiently planning in dynamic and uncertain conditions. Given the significant variability of environments, human operators often have…

机器人学 · 计算机科学 2025-03-11 Shuhao Liao , Xuxin Lv , Yuhong Cao , Jeric Lew , Wenjun Wu , Guillaume Sartoretti

Despite the remarkable code generation abilities of large language models LLMs, they still face challenges in complex task handling. Robot development, a highly intricate field, inherently demands human involvement in task allocation and…

机器人学 · 计算机科学 2024-02-19 Zhirong Luan , Yujun Lai , Rundong Huang , Xiaruiqi Lan , Liangjun Chen , Badong Chen

Accurate prediction of human behavior is crucial for AI systems to effectively support real-world applications, such as autonomous robots anticipating and assisting with human tasks. Real-world scenarios frequently present challenges such…

人机交互 · 计算机科学 2025-07-21 Kojiro Takeyama , Yimeng Liu , Misha Sra

Large language models (LLMs) can handle a wide variety of general tasks with simple prompts, without the need for task-specific training. Multimodal Large Language Models (MLLMs), built upon LLMs, have demonstrated impressive potential in…

Large language models (LLMs) and large multimodal models (LMMs) have achieved unprecedented breakthrough, showcasing remarkable capabilities in natural language understanding, generation, and complex reasoning. This transformative potential…

机器学习 · 计算机科学 2025-10-24 Hyun Jong Yang , Hyunsoo Kim , Hyeonho Noh , Seungnyun Kim , Byonghyo Shim

The task of long-term action anticipation demands solutions that can effectively model temporal dynamics over extended periods while deeply understanding the inherent semantics of actions. Traditional approaches, which primarily rely on…

计算机视觉与模式识别 · 计算机科学 2025-01-03 Binglu Wang , Yao Tian , Shunzhou Wang , Le Yang

To achieve natural and intuitive interaction with people, HRI frameworks combine a wide array of methods for human perception, intention communication, human-aware navigation and collaborative action. In practice, when encountering…

机器人学 · 计算机科学 2025-01-22 Tim Schreiter , Jens V. Rüppel , Rishi Hazra , Andrey Rudenko , Martin Magnusson , Achim J. Lilienthal

Large language models (LLMs) have notably accelerated progress towards artificial general intelligence (AGI), with their impressive zero-shot capacity for user-tailored tasks, endowing them with immense potential across a range of…

计算机视觉与模式识别 · 计算机科学 2023-05-26 Wenhai Wang , Zhe Chen , Xiaokang Chen , Jiannan Wu , Xizhou Zhu , Gang Zeng , Ping Luo , Tong Lu , Jie Zhou , Yu Qiao , Jifeng Dai

In the realm of Business Process Management (BPM), process modeling plays a crucial role in translating complex process dynamics into comprehensible visual representations, facilitating the understanding, analysis, improvement, and…

软件工程 · 计算机科学 2024-07-01 Humam Kourani , Alessandro Berti , Daniel Schuster , Wil M. P. van der Aalst

The emergence of large language models (LLMs) opens new frontiers for unmanned aerial vehicle (UAVs), yet existing systems remain confined to predefined tasks due to hardware-software co-design challenges. This paper presents the first…

机器人学 · 计算机科学 2025-03-12 Ji Zhao , Xiao Lin

Simulations, although powerful in accurately replicating real-world systems, often remain inaccessible to non-technical users due to their complexity. Conversely, large language models (LLMs) provide intuitive, language-based interactions…

计算与语言 · 计算机科学 2025-05-22 Jacob Kleiman , Kevin Frank , Joseph Voyles , Sindy Campagna

Large language models (LLMs) can reproduce a wide variety of rhetorical styles and generate text that expresses a broad spectrum of sentiments. This capacity, now available at low cost, makes them powerful tools for manipulation and…

社会与信息网络 · 计算机科学 2024-05-08 Yaqub Chaudhary , Jonnie Penn

Simulating learner actions helps stress-test open-ended interactive learning environments and prototype new adaptations before deployment. While recent studies show the promise of using large language models (LLMs) for simulating human…

人工智能 · 计算机科学 2024-10-15 Amogh Mannekote , Adam Davies , Jina Kang , Kristy Elizabeth Boyer

Instructing a robot to complete an everyday task within our homes has been a long-standing challenge for robotics. While recent progress in language-conditioned imitation learning and offline reinforcement learning has demonstrated…

机器人学 · 计算机科学 2024-07-03 Federico Ceola , Lorenzo Natale , Niko Sünderhauf , Krishan Rana

Large Language Models (LLMs) are transforming artificial intelligence, enabling autonomous agents to perform diverse tasks across various domains. These agents, proficient in human-like text comprehension and generation, have the potential…

人工智能 · 计算机科学 2024-04-10 Saikat Barua

Though large language models (LLMs) have enabled great success across a wide variety of tasks, they still appear to fall short of one of the loftier goals of artificial intelligence research: creating an artificial system that can adapt its…

计算与语言 · 计算机科学 2026-05-04 Michael A. Lepori , Tal Linzen , Ann Yuan , Katja Filippova

Large Language Models (LLMs) have demonstrated their remarkable capabilities in numerous fields. This survey focuses on how LLMs empower users, regardless of their technical background, to use human languages to automatically generate…

软件工程 · 计算机科学 2025-04-03 Nam Huynh , Beiyu Lin

Multimodal Large Language Models (MLLMs) have shown impressive reasoning abilities and general intelligence in various domains. It inspires researchers to train end-to-end MLLMs or utilize large models to generate policies with…

机器人学 · 计算机科学 2024-07-31 Qi Lv , Hao Li , Xiang Deng , Rui Shao , Michael Yu Wang , Liqiang Nie

A prior-informed large language model (LLM) driven multi-task learning framework is proposed for the unified description of multiple nuclear observables. By fine-tuning the pre-trained DeepSeek-R1-1.5B model with Low-Rank Adaptation (LoRA),…

核理论 · 物理学 2026-05-29 S. J. Guo , S. Y. Wang , E. H. Wang , Z. M. Niu , Y. M. Ding