103 citations · 163 across the 4 of their papers we have counts for
4 papers
R+X: Retrieval and Execution from Everyday Human Videos
Georgios Papagiannis, Norman Di Palo, Pietro Vitiello +1
We present R+X, a framework which enables robots to learn skills from long, unlabelled, first-person videos of humans performing everyday tasks. Given a language command from a hum…
Keypoint Action Tokens Enable In-Context Imitation Learning in Robotics
Norman Di Palo, Edward Johns
We show that off-the-shelf text-based Transformers, with no additional training, can perform few-shot in-context visual imitation learning, mapping visual observations to action se…
Language Models as Zero-Shot Trajectory Generators
Teyun Kwon, Norman Di Palo, Edward Johns
Large Language Models (LLMs) have recently shown promise as high-level planners for robots when given access to a selection of low-level skills. However, it is often assumed that L…
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…