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

Embodied AI aims to create agents that complete complex tasks by interacting with the
environment. A key problem in this field is embodied navigation which understands multi …

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

We introduce the task of localizing a flexible number of objects in real-world 3D scenes
using natural language descriptions. Existing 3D visual grounding tasks focus on localizing …

Remarkable progress has been made in recent years in the fields of vision, language, and
robotics. We now have vision models capable of recognizing objects based on language …

Vision-Language-Action (VLA) models have shown remarkable potential in visuomotor
control and instruction comprehension through end-to-end learning processes. However …

Vision-and-Language Navigation (VLN) has gained increasing attention over recent years
and many approaches have emerged to advance their development. The remarkable …

The Embodied AI community has recently made significant strides in visual navigation tasks
exploring targets from 3D coordinates objects language description and images. However …

Deploying robots in open-ended unstructured environments such as homes has been a long-
standing research problem. However, robots are often studied only in closed-off lab settings …

We present PoliFormer (Policy Transformer), an RGB-only indoor navigation agent trained
end-to-end with reinforcement learning at scale that generalizes to the real-world without …