291 citations · 788 across the 42 of their papers we have counts for
7 papers · 1 filter
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…
Aria-NeRF: Multimodal Egocentric View Synthesis
Jiankai Sun, Jianing Qiu, Chuanyang Zheng +3
We seek to accelerate research in developing rich, multimodal scene models trained from egocentric data, based on differentiable volumetric ray-tracing inspired by Neural Radiance…
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…
Connected Autonomous Vehicle Motion Planning with Video Predictions from Smart, Self-Supervised Infrastructure
Jiankai Sun, Shreyas Kousik, David Fridovich-Keil +1
Connected autonomous vehicles (CAVs) promise to enhance safety, efficiency, and sustainability in urban transportation. However, this is contingent upon a CAV correctly predicting…
Lyra: Orchestrating Dual Correction in Automated Theorem Proving
Chuanyang Zheng, Haiming Wang, Enze Xie +6
Large Language Models (LLMs) present an intriguing avenue for exploration in the field of formal theorem proving. Nevertheless, their full potential, particularly concerning the mi…
Large AI Models in Health Informatics: Applications, Challenges, and the Future
Jianing Qiu, Lin Li, Jiankai Sun +11
Large AI models, or foundation models, are models recently emerging with massive scales both parameter-wise and data-wise, the magnitudes of which can reach beyond billions. Once p…
MimicPlay: Long-Horizon Imitation Learning by Watching Human Play
Chen Wang, Linxi Fan, Jiankai Sun +5
Imitation learning from human demonstrations is a promising paradigm for teaching robots manipulation skills in the real world. However, learning complex long-horizon tasks often r…