State-of-the-art Large Multi-Modal Models (LMMs) have demonstrated exceptional
capabilities in vision-language tasks. Despite their advanced functionalities, the …

This survey presents an overview of methods for learning from video (LfV) in the context of
reinforcement learning (RL) and robotics. We focus on methods capable of scaling to large …

We consider the problem of Embodied Question Answering (EQA), which refers to settings
where an embodied agent such as a robot needs to actively explore an environment to …

Large vision language models (LVLMs) often suffer from object hallucination, producing
objects not present in the given images. While current benchmarks for object hallucination …

Legged robots are physically capable of navigating a diverse variety of environments and
overcoming a wide range of obstructions. For example, in a search and rescue mission, a …

We present VisionLLM v2, an end-to-end generalist multimodal large model (MLLM) that
unifies visual perception, understanding, and generation within a single framework. Unlike …

Image-generation diffusion models have been fine-tuned to unlock new capabilities such as
image-editing and novel view synthesis. Can we similarly unlock image-generation models …

Vision Language Models (VLMs) have demonstrated remarkable performance in 2D vision
and language tasks. However, their ability to reason about spatial arrangements remains …

The ultimate goals of robotic learning is to acquire a comprehensive and generalizable
robotic system capable of performing both seen skills within the training distribution and …

From rearranging objects on a table to putting groceries into shelves, robots must plan
precise action points to perform tasks accurately and reliably. In spite of the recent adoption …