Abstract Automated Planning and Scheduling is among the growing areas in Artificial
Intelligence (AI) where mention of LLMs has gained popularity. Based on a comprehensive …

We introduce a pipeline that enhances a general-purpose Vision Language Model, GPT-4V
(ision), to facilitate one-shot visual teaching for robotic manipulation. This system analyzes …

The rapid evolution of network technologies and the growing complexity of network tasks
necessitate a paradigm shift in how networks are designed, configured, and managed. With …

The advancement of function-calling agent models requires diverse, reliable, and high-
quality datasets. This paper presents APIGen, an automated data generation pipeline …

The development of artificial intelligence systems is transitioning from creating static, task-
specific models to dynamic, agent-based systems capable of performing well in a wide …

We investigate the extent to which contemporary Large Language Models (LLMs) can
engage in exploration, a core capability in reinforcement learning and decision making. We …

Training general robotic policies from heterogeneous data for different tasks is a significant
challenge. Existing robotic datasets vary in different modalities such as color, depth, tactile …

The realization of universal robots is an ultimate goal of researchers. However, a key hurdle
in achieving this goal lies in the robots' ability to manipulate objects in their unstructured …

Learning from human demonstrations is an emerging trend for designing intelligent robotic
systems. However, previous methods typically regard videos as instructions, simply dividing …

Character animation in real-world scenarios necessitates a variety of constraints such as
trajectories key-frames interactions etc. Existing methodologies typically treat single or a …