5 papers
Scaling Synthetic Task Generation for Agents via Exploration
Ram Ramrakhya, Andrew Szot, Omar Attia +6
Post-Training Multimodal Large Language Models (MLLMs) to build interactive agents holds promise across domains such as computer-use, web navigation, and robotics. A key challenge…
Ferret-UI 2: Mastering Universal User Interface Understanding Across Platforms
Zhangheng Li, Keen You, Haotian Zhang +7
Building a generalist model for user interface (UI) understanding is challenging due to various foundational issues, such as platform diversity, resolution variation, and data limi…
DSplats: 3D Generation by Denoising Splats-Based Multiview Diffusion Models
Kevin Miao, Harsh Agrawal, Qihang Zhang +4
Generating high-quality 3D content requires models capable of learning robust distributions of complex scenes and the real-world objects within them. Recent Gaussian-based 3D recon…
From Multimodal LLMs to Generalist Embodied Agents: Methods and Lessons
Andrew Szot, Bogdan Mazoure, Omar Attia +6
We examine the capability of Multimodal Large Language Models (MLLMs) to tackle diverse domains that extend beyond the traditional language and vision tasks these models are typica…
Grounding Multimodal Large Language Models in Actions
Andrew Szot, Bogdan Mazoure, Harsh Agrawal +3
Multimodal Large Language Models (MLLMs) have demonstrated a wide range of capabilities across many domains, including Embodied AI. In this work, we study how to best ground a MLLM…