Turnitin
降AI改写
早检测系统
早降重系统
Turnitin-UK版
万方检测-期刊版
维普编辑部版
Grammarly检测
Paperpass检测
checkpass检测
PaperYY检测
Large language models for robotics: A survey
The human ability to learn, generalize, and control complex manipulation tasks through multi-
modality feedback suggests a unique capability, which we refer to as dexterity intelligence …
modality feedback suggests a unique capability, which we refer to as dexterity intelligence …
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 …
Lm-nav: Robotic navigation with large pre-trained models of language, vision, and action
Goal-conditioned policies for robotic navigation can be trained on large, unannotated
datasets, providing for good generalization to real-world settings. However, particularly in …
datasets, providing for good generalization to real-world settings. However, particularly in …
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 …
Q-transformer: Scalable offline reinforcement learning via autoregressive q-functions
In this work, we present a scalable reinforcement learning method for training multi-task
policies from large offline datasets that can leverage both human demonstrations and …
policies from large offline datasets that can leverage both human demonstrations and …
On the opportunities and risks of foundation models
AI is undergoing a paradigm shift with the rise of models (eg, BERT, DALL-E, GPT-3) that are
trained on broad data at scale and are adaptable to a wide range of downstream tasks. We …
trained on broad data at scale and are adaptable to a wide range of downstream tasks. We …
Causal machine learning: A survey and open problems
Causal Machine Learning (CausalML) is an umbrella term for machine learning methods
that formalize the data-generation process as a structural causal model (SCM). This …
that formalize the data-generation process as a structural causal model (SCM). This …
Contrastive learning as goal-conditioned reinforcement learning
In reinforcement learning (RL), it is easier to solve a task if given a good representation.
While deep RL should automatically acquire such good representations, prior work often …
While deep RL should automatically acquire such good representations, prior work often …
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 …
Hiql: Offline goal-conditioned rl with latent states as actions
Unsupervised pre-training has recently become the bedrock for computer vision and natural
language processing. In reinforcement learning (RL), goal-conditioned RL can potentially …
language processing. In reinforcement learning (RL), goal-conditioned RL can potentially …