Recent developments in foundation models, like Large Language Models (LLMs) and Vision-
Language Models (VLMs), trained on extensive data, facilitate flexible application across …

This paper presents ShapeLLM, the first 3D Multimodal Large Language Model (LLM)
designed for embodied interaction, exploring a universal 3D object understanding with 3D …

Abstract 3D vision-language (3D-VL) grounding, which aims to align language with 3D
physical environments, stands as a cornerstone in develo** embodied agents. In …

For years, researchers have been devoted to generalizable object perception and
manipulation, where cross-category generalizability is highly desired yet underexplored. In …

One of the roadblocks for training generalist robotic models today is heterogeneity. Previous
robot learning methods often collect data to train with one specific embodiment for one task …

Robot manipulation relies on accurately predicting contact points and end-effector directions
to ensure successful operation. However learning-based robot manipulation trained on a …

We show that large language models (LLMs) can be adapted to be generalizable policies
for embodied visual tasks. Our approach, called Large LAnguage model Reinforcement …

Leveraging massive knowledge and learning schemes from large language models (LLMs),
recent machine learning models show notable successes in building generalist agents that …

This work proposes a retrieve-and-transfer framework for zero-shot robotic manipulation,
dubbed RAM, featuring generalizability across various objects, environments, and …

With recent developments in Embodied Artificial Intelligence (EAI) research there has been
a growing demand for high-quality large-scale interactive scene generation. While prior …