Large, high-capacity models trained on diverse datasets have shown remarkable successes
on efficiently tackling downstream applications. In domains from NLP to Computer Vision, …

Large, high-capacity models trained on diverse datasets have shown remarkable successes
on efficiently tackling downstream applications. In domains from NLP to Computer Vision, …

We present a system for non-prehensile manipulation that require a significant number of
contact mode transitions and the use of environmental contacts to successfully manipulate an …

Large, high-capacity models trained on diverse datasets have shown remarkable successes
on efficiently tackling downstream applications. In domains from NLP to Computer Vision, …

Nonprehensile manipulation is essential for manipulating objects that are too thin, large, or
otherwise ungraspable in the wild. To sidestep the difficulty of contact modeling in …

Modern robots vary significantly in shape, size, and sensor configurations used to perceive
and interact with their environments. However, most navigation policies are embodiment-…

Foundation models pretrained on diverse datasets excel in various tasks but face challenges
in real-world applications, particularly in sequential decision-making under uncertainty. …

This book presents the combined proceedings of the 15th International Conference on
Computer Science and its Applications (CSA 2023) and the 17th KIPS International Conference …

In recent years, the rapid advancement of Large Language Models (LLMs) such as the
Generative Pre-trained Transformer (GPT) has attracted increasing attention due to their potential …

Spatial understanding is a crucial capability for robots to make grounded decisions based
on their environment. This foundational skill enables robots not only to perceive their …