356 citations · 633 across the 28 of their papers we have counts for
6 papers · 1 filter
Geometry Matching for Multi-Embodiment Grasping
Maria Attarian, Muhammad Adil Asif, Jingzhou Liu +4
Many existing learning-based grasping approaches concentrate on a single embodiment, provide limited generalization to higher DoF end-effectors and cannot capture a diverse set of…
Video Language Planning
Yilun Du, Mengjiao Yang, Pete Florence +10
We are interested in enabling visual planning for complex long-horizon tasks in the space of generated videos and language, leveraging recent advances in large generative models pr…
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…
Learning Interactive Real-World Simulators
Sherry Yang, Yilun Du, Kamyar Ghasemipour +4
Generative models trained on internet data have revolutionized how text, image, and video content can be created. Perhaps the next milestone for generative models is to simulate re…
PaLM-E: An Embodied Multimodal Language Model
Danny Driess, Fei Xia, Mehdi S. M. Sajjadi +19
Large language models excel at a wide range of complex tasks. However, enabling general inference in the real world, e.g., for robotics problems, raises the challenge of grounding.…
Scaling Robot Learning with Semantically Imagined Experience
Tianhe Yu, Ted Xiao, Austin Stone +10
Recent advances in robot learning have shown promise in enabling robots to perform a variety of manipulation tasks and generalize to novel scenarios. One of the key contributing fa…