112 citations · 304 across the 149 of their papers we have counts for
12 papers · 1 filter
SkyScript: A Large and Semantically Diverse Vision-Language Dataset for Remote Sensing
Zhecheng Wang, Rajanie Prabha, Tianyuan Huang +2
Remote sensing imagery, despite its broad applications in helping achieve Sustainable Development Goals and tackle climate change, has not yet benefited from the recent advancement…
Model-Based Control with Sparse Neural Dynamics
Ziang Liu, Genggeng Zhou, Jeff He +4
Learning predictive models from observations using deep neural networks (DNNs) is a promising new approach to many real-world planning and control problems. However, common DNNs ar…
Foundation Models in Robotics: Applications, Challenges, and the Future
Roya Firoozi, Johnathan Tucker, Stephen Tian +12
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 li…
Ponymation: Learning Articulated 3D Animal Motions from Unlabeled Online Videos
Keqiang Sun, Dor Litvak, Yunzhi Zhang +3
We introduce a new method for learning a generative model of articulated 3D animal motions from raw, unlabeled online videos. Unlike existing approaches for 3D motion synthesis, ou…
Holodeck: Language Guided Generation of 3D Embodied AI Environments
Yue Yang, Fan-Yun Sun, Luca Weihs +11
3D simulated environments play a critical role in Embodied AI, but their creation requires expertise and extensive manual effort, restricting their diversity and scope. To mitigate…
NOIR: Neural Signal Operated Intelligent Robots for Everyday Activities
Ruohan Zhang, Sharon Lee, Minjune Hwang +11
We present Neural Signal Operated Intelligent Robots (NOIR), a general-purpose, intelligent brain-robot interface system that enables humans to command robots to perform everyday a…
Learning Sequential Acquisition Policies for Robot-Assisted Feeding
Priya Sundaresan, Jiajun Wu, Dorsa Sadigh
A robot providing mealtime assistance must perform specialized maneuvers with various utensils in order to pick up and feed a range of food items. Beyond these dexterous low-level…
Open X-Embodiment: Robotic Learning Datasets and RT-X Models
Embodiment Collaboration, Abby O'Neill, Abdul Rehman +291
Large, high-capacity models trained on diverse datasets have shown remarkable successes on efficiently tackling downstream applications. In domains from NLP to Computer Vision, thi…
Mini-BEHAVIOR: A Procedurally Generated Benchmark for Long-horizon Decision-Making in Embodied AI
Emily Jin, Jiaheng Hu, Zhuoyi Huang +4
We present Mini-BEHAVIOR, a novel benchmark for embodied AI that challenges agents to use reasoning and decision-making skills to solve complex activities that resemble everyday hu…
Physically Grounded Vision-Language Models for Robotic Manipulation
Jensen Gao, Bidipta Sarkar, Fei Xia +5
Recent advances in vision-language models (VLMs) have led to improved performance on tasks such as visual question answering and image captioning. Consequently, these models are no…
FedLE: Federated Learning Client Selection with Lifespan Extension for Edge IoT Networks
Jiajun Wu, Steve Drew, Jiayu Zhou
Federated learning (FL) is a distributed and privacy-preserving learning framework for predictive modeling with massive data generated at the edge by Internet of Things (IoT) devic…
Topology-aware Federated Learning in Edge Computing: A Comprehensive Survey
Jiajun Wu, Steve Drew, Fan Dong +2
The ultra-low latency requirements of 5G/6G applications and privacy constraints call for distributed machine learning systems to be deployed at the edge. With its simple yet effec…