Large language models (LLMs) have gained increasing popularity in robotic task planning
due to their exceptional abilities in text analytics and generation, as well as their broad …

Autonomous robot navigation and manipulation in open environments require reasoning
and replanning with closed-loop feedback. We present COME-robot, the first closed-loop …

Chemistry experimentation is often resource-and labor-intensive. Despite the many benefits
incurred by the integration of advanced and special-purpose lab equipment, many aspects …

Robotic manipulation in open-world settings requires not only task execution but also the
ability to detect and learn from failures. While recent advances in vision-language models …

Visual imitation learning (VIL) provides an efficient and intuitive strategy for robotic systems
to acquire novel skills. Recent advancements in Vision Language Models (VLMs) have …

Vision-Language Models (VLM) can generate plausible high-level plans when prompted
with a goal, the context, an image of the scene, and any planning constraints. However …

In recent years, studies have been actively conducted on combining large language models
(LLM) and robotics; however, most have not considered end-to-end feed-back in the robot …

With their prominent scene understanding and reasoning capabilities, pre-trained visual-
language models (VLMs) such as GPT-4V have attracted increasing attention in robotic task …

In this work, we present SuFIA, the first framework for natural language-guided augmented
dexterity for robotic surgical assistants. SuFIA incorporates the strong reasoning capabilities …

In this paper, we discuss approaches for integrating Computational Creativity (CC) with
research in large language and vision models (LLVMs) to address a key limitation of these …