Bridge data: Boosting generalization of robotic skills with cross-domain datasets
Robot learning holds the promise of learning policies that generalize broadly. However,
such generalization requires sufficiently diverse datasets of the task of interest, which can be …
such generalization requires sufficiently diverse datasets of the task of interest, which can be …
Pre-training for robots: Offline rl enables learning new tasks from a handful of trials
Progress in deep learning highlights the tremendous potential of utilizing diverse robotic
datasets for attaining effective generalization and makes it enticing to consider leveraging …
datasets for attaining effective generalization and makes it enticing to consider leveraging …
Generalization with lossy affordances: Leveraging broad offline data for learning visuomotor tasks
The use of broad datasets has proven to be crucial for generalization for a wide range of
fields. However, how to effectively make use of diverse multi-task data for novel downstream …
fields. However, how to effectively make use of diverse multi-task data for novel downstream …
Robonet: Large-scale multi-robot learning
Robot learning has emerged as a promising tool for taming the complexity and diversity of
the real world. Methods based on high-capacity models, such as deep networks, hold the …
the real world. Methods based on high-capacity models, such as deep networks, hold the …
Bridgedata v2: A dataset for robot learning at scale
We introduce BridgeData V2, a large and diverse dataset of robotic manipulation behaviors
designed to facilitate research in scalable robot learning. BridgeData V2 contains 53,896 …
designed to facilitate research in scalable robot learning. BridgeData V2 contains 53,896 …
Gnfactor: Multi-task real robot learning with generalizable neural feature fields
It is a long-standing problem in robotics to develop agents capable of executing diverse
manipulation tasks from visual observations in unstructured real-world environments. To …
manipulation tasks from visual observations in unstructured real-world environments. To …
Rt-1: Robotics transformer for real-world control at scale
A Brohan, N Brown, J Carbajal, Y Chebotar… - arXiv preprint arXiv …, 2022 - arxiv.org
By transferring knowledge from large, diverse, task-agnostic datasets, modern machine
learning models can solve specific downstream tasks either zero-shot or with small task …
learning models can solve specific downstream tasks either zero-shot or with small task …
Robel: Robotics benchmarks for learning with low-cost robots
ROBEL is an open-source platform of cost-effective robots designed for reinforcement
learning in the real world. ROBEL introduces two robots, each aimed to accelerate …
learning in the real world. ROBEL introduces two robots, each aimed to accelerate …
Open x-embodiment: Robotic learning datasets and rt-x models
Large, high-capacity models trained on diverse datasets have shown remarkable successes
on efficiently tackling downstream applications. In domains from NLP to Computer Vision …
on efficiently tackling downstream applications. In domains from NLP to Computer Vision …
Robotic skill acquisition via instruction augmentation with vision-language models
In recent years, much progress has been made in learning robotic manipulation policies that
follow natural language instructions. Such methods typically learn from corpora of robot …
follow natural language instructions. Such methods typically learn from corpora of robot …