We survey applications of pretrained foundation models in robotics. Traditional deep
learning models in robotics are trained on small datasets tailored for specific tasks, which …

Hallucinations is a big shadow hanging over the rapidly evolving multimodal large language
models (MLLMs), referring to that the generated text is inconsistent with the image content …

Transformers have become one of the foundational architectures in point cloud analysis
tasks due to their excellent global modeling ability. However, the attention mechanism has …

Advanced 3D object detection methods usually rely on large-scale, elaborately labeled
datasets to achieve good performance. However, labeling the bounding boxes for the 3D …

Zero-shot 6D object pose estimation involves the detection of novel objects with their 6D
poses in cluttered scenes presenting significant challenges for model generalizability …

Segment anything model (SAM) developed by Meta AI Research has recently attracted
significant attention. Trained on a large segmentation dataset of over 1 billion masks, SAM is …

Recent advancements in 3D Large Language Models (LLMs) have demonstrated promising
capabilities for 3D scene understanding. However, previous methods exhibit deficiencies in …

Recent research has evidenced the significant potentials of Large Language Models (LLMs)
in handling challenging tasks within 3D scenes. However, current models are constrained to …

Deep recognition models are widely vulnerable to adversarial examples, which change the
model output by adding quasi-imperceptible perturbation to the image input. Recently …

The rise of large foundation models, trained on extensive datasets, is revolutionizing the
field of AI. Models such as SAM, DALL-E2, and GPT-4 showcase their adaptability by …