[HTML][HTML] Embracing change: Continual learning in deep neural networks
Artificial intelligence research has seen enormous progress over the past few decades, but it
predominantly relies on fixed datasets and stationary environments. Continual learning is an …
predominantly relies on fixed datasets and stationary environments. Continual learning is an …
A review of reinforcement learning based energy management systems for electrified powertrains: Progress, challenge, and potential solution
AH Ganesh, B Xu - Renewable and Sustainable Energy Reviews, 2022 - Elsevier
The impact of internal combustion engine-powered automobiles on climate change due to
emissions and the depletion of fossil fuels has contributed to the progress of electrified …
emissions and the depletion of fossil fuels has contributed to the progress of electrified …
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 …
Perceiver-actor: A multi-task transformer for robotic manipulation
Transformers have revolutionized vision and natural language processing with their ability to
scale with large datasets. But in robotic manipulation, data is both limited and expensive …
scale with large datasets. But in robotic manipulation, data is both limited and expensive …
A generalist agent
Inspired by progress in large-scale language modeling, we apply a similar approach
towards building a single generalist agent beyond the realm of text outputs. The agent …
towards building a single generalist agent beyond the realm of text outputs. The agent …
Embodiedgpt: Vision-language pre-training via embodied chain of thought
Embodied AI is a crucial frontier in robotics, capable of planning and executing action
sequences for robots to accomplish long-horizon tasks in physical environments. In this …
sequences for robots to accomplish long-horizon tasks in physical environments. In this …
Scaling up and distilling down: Language-guided robot skill acquisition
We present a framework for robot skill acquisition, which 1) efficiently scale up data
generation of language-labelled robot data and 2) effectively distills this data down into a …
generation of language-labelled robot data and 2) effectively distills this data down into a …
R3m: A universal visual representation for robot manipulation
We study how visual representations pre-trained on diverse human video data can enable
data-efficient learning of downstream robotic manipulation tasks. Concretely, we pre-train a …
data-efficient learning of downstream robotic manipulation tasks. Concretely, we pre-train a …
Rlprompt: Optimizing discrete text prompts with reinforcement learning
Prompting has shown impressive success in enabling large pretrained language models
(LMs) to perform diverse NLP tasks, especially when only few downstream data are …
(LMs) to perform diverse NLP tasks, especially when only few downstream data are …
Multi-game decision transformers
A longstanding goal of the field of AI is a method for learning a highly capable, generalist
agent from diverse experience. In the subfields of vision and language, this was largely …
agent from diverse experience. In the subfields of vision and language, this was largely …