Self-adaptive systems (SASs) are designed to handle changes and uncertainties through a
feedback loop with four core functionalities: monitoring, analyzing, planning, and execution …

Since the emergence of ChatGPT, research on large language models (LLMs) has actively
progressed across various fields. LLMs, pre-trained on vast text datasets, have exhibited …

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 …

Monocular depth estimation is a challenging task that predicts the pixel-wise depth from a
single 2D image. Current methods typically model this problem as a regression or …

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 …

Conventional state representations in reinforcement learning often omit critical task-related
details, presenting a significant challenge for value networks in establishing accurate …

Physically-simulated models for human motion can generate high-quality responsive
character animations, often in real-time. Natural language serves as a flexible interface for …

This work focuses on generating realistic, physically-based human behaviors from multi-
modal inputs, which may only partially specify the desired motion. For example, the input …

Humanoid robots have great potential to perform various human-level skills. These skills
involve locomotion, manipulation, and cognitive capabilities. Driven by advances in machine …

Foundation models (FMs), large deep learning models pre-trained on vast, unlabeled
datasets, exhibit powerful capabilities in understanding complex patterns and generating …