39 citations · 87 across the 19 of their papers we have counts for
9 papers · 1 filter
PolaRiS: Scalable Real-to-Sim Evaluations for Generalist Robot Policies
Arhan Jain, Mingtong Zhang, Kanav Arora +11
A significant challenge for robot learning research is our ability to accurately measure and compare the performance of robot policies. Benchmarking in robotics is historically cha…
Robot Learning from a Physical World Model
Jiageng Mao, Sicheng He, Hao-Ning Wu +9
We introduce PhysWorld, a framework that enables robot learning from video generation through physical world modeling. Recent video generation models can synthesize photorealistic…
ROVER: Benchmarking Reciprocal Cross-Modal Reasoning for Omnimodal Generation
Yongyuan Liang, Wei Chow, Feng Li +7
Unified multimodal models (UMMs) have emerged as a powerful paradigm for seamlessly unifying text and image understanding and generation. However, prevailing evaluations treat thes…
Robot Learning from Any Images
Siheng Zhao, Jiageng Mao, Wei Chow +11
We introduce RoLA, a framework that transforms any in-the-wild image into an interactive, physics-enabled robotic environment. Unlike previous methods, RoLA operates directly on a…
Sensing, Social, and Motion Intelligence in Embodied Navigation: A Comprehensive Survey
Chaoran Xiong, Yulong Huang, Fangwen Yu +4
Embodied navigation (EN) advances traditional navigation by enabling robots to perform complex egocentric tasks through sensing, social, and motion intelligence. In contrast to cla…
ManipBench: Benchmarking Vision-Language Models for Low-Level Robot Manipulation
Enyu Zhao, Vedant Raval, Hejia Zhang +5
Vision-Language Models (VLMs) have revolutionized artificial intelligence and robotics due to their commonsense reasoning capabilities. In robotic manipulation, VLMs are used prima…
Domain-Conditioned Scene Graphs for State-Grounded Task Planning
Jonas Herzog, Jiangpin Liu, Yue Wang
Recent robotic task planning frameworks have integrated large multimodal models (LMMs) such as GPT-4o. To address grounding issues of such models, it has been suggested to split th…
UrbanVideo-Bench: Benchmarking Vision-Language Models on Embodied Intelligence with Video Data in Urban Spaces
Baining Zhao, Jianjie Fang, Zichao Dai +8
Large multimodal models exhibit remarkable intelligence, yet their embodied cognitive abilities during motion in open-ended urban 3D space remain to be explored. We introduce a ben…
PhysBench: Benchmarking and Enhancing Vision-Language Models for Physical World Understanding
Wei Chow, Jiageng Mao, Boyi Li +3
Understanding the physical world is a fundamental challenge in embodied AI, critical for enabling agents to perform complex tasks and operate safely in real-world environments. Whi…