Towards continual reinforcement learning: A review and perspectives
In this article, we aim to provide a literature review of different formulations and approaches
to continual reinforcement learning (RL), also known as lifelong or non-stationary RL. We …
to continual reinforcement learning (RL), also known as lifelong or non-stationary RL. We …
Deep learning in mobile and wireless networking: A survey
The rapid uptake of mobile devices and the rising popularity of mobile applications and
services pose unprecedented demands on mobile and wireless networking infrastructure …
services pose unprecedented demands on mobile and wireless networking infrastructure …
Describe, explain, plan and select: Interactive planning with large language models enables open-world multi-task agents
We investigate the challenge of task planning for multi-task embodied agents in open-world
environments. Two main difficulties are identified: 1) executing plans in an open-world …
environments. Two main difficulties are identified: 1) executing plans in an open-world …
Do as i can, not as i say: Grounding language in robotic affordances
M Ahn, A Brohan, N Brown, Y Chebotar… - arXiv preprint arXiv …, 2022 - arxiv.org
Large language models can encode a wealth of semantic knowledge about the world. Such
knowledge could be extremely useful to robots aiming to act upon high-level, temporally …
knowledge could be extremely useful to robots aiming to act upon high-level, temporally …
Video pretraining (vpt): Learning to act by watching unlabeled online videos
Pretraining on noisy, internet-scale datasets has been heavily studied as a technique for
training models with broad, general capabilities for text, images, and other modalities …
training models with broad, general capabilities for text, images, and other modalities …
Describe, explain, plan and select: interactive planning with llms enables open-world multi-task agents
In this paper, we study the problem of planning in Minecraft, a popular, democratized yet
challenging open-ended environment for developing multi-task embodied agents. We've …
challenging open-ended environment for developing multi-task embodied agents. We've …
Jarvis-1: Open-world multi-task agents with memory-augmented multimodal language models
Achieving human-like planning and control with multimodal observations in an open world is
a key milestone for more functional generalist agents. Existing approaches can handle …
a key milestone for more functional generalist agents. Existing approaches can handle …
The neuro-symbolic concept learner: Interpreting scenes, words, and sentences from natural supervision
We propose the Neuro-Symbolic Concept Learner (NS-CL), a model that learns visual
concepts, words, and semantic parsing of sentences without explicit supervision on any of …
concepts, words, and semantic parsing of sentences without explicit supervision on any of …
A survey of zero-shot learning: Settings, methods, and applications
Most machine-learning methods focus on classifying instances whose classes have already
been seen in training. In practice, many applications require classifying instances whose …
been seen in training. In practice, many applications require classifying instances whose …
Calvin: A benchmark for language-conditioned policy learning for long-horizon robot manipulation tasks
General-purpose robots coexisting with humans in their environment must learn to relate
human language to their perceptions and actions to be useful in a range of daily tasks …
human language to their perceptions and actions to be useful in a range of daily tasks …