In this study, we are interested in imbuing robots with the capability of physically-grounded
task planning. Recent advancements have shown that large language models (LLMs) …

Performing language-conditioned robotic manipulation tasks in unstructured environments
is highly demanded for general intelligent robots. Conventional robotic manipulation …

Foundation models pre-trained on web-scale data are shown to encapsulate extensive
world knowledge beneficial for robotic manipulation in the form of task planning. However …

We present an exciting journey from 3D point‐cloud data (PCD) to the state of the art in
graph neural networks (GNNs) and their evolution with explainable artificial intelligence …

Learning generalizable visual representations from Internet data has yielded promising
results for robotics. Yet prevailing approaches focus on pre-training 2D representations …

Precise robot manipulations require rich spatial information in imitation learning. Image-
based policies model object positions from fixed cameras, which are sensitive to camera …

Acquiring a multi-task imitation policy in 3D manipulation poses challenges in terms of
scene understanding and action prediction. Current methods employ both 3D representation …

Generalization in robotic manipulation remains a critical challenge, particularly when scaling
to new environments with limited demonstrations. This paper introduces CAGE, a novel …

Generalization is a pivotal challenge for agents following natural language instructions. To
approach this goal, we leverage a vision-language model (VLM) for visual grounding and …

Given the high cost of collecting robotic data in the real world, sample efficiency is a
consistently compelling pursuit in robotics. In this paper, we introduce SGRv2, an imitation …