103 citations · 169 across the 5 of their papers we have counts for
5 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…
Regularizing Trajectory Optimization with Denoising Autoencoders
Rinu Boney, Norman Di Palo, Mathias Berglund +4
Trajectory optimization using a learned model of the environment is one of the core elements of model-based reinforcement learning. This procedure often suffers from exploiting ina…