112 citations · 304 across the 149 of their papers we have counts for
6 papers · 2 filters
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…
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…
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…