A longstanding goal of the field of AI is a strategy for compiling diver...
Large language models can encode a wealth of semantic knowledge about th...
In this work we investigate and demonstrate benefits of a Bayesian appro...
In this paper, we study the problem of enabling a vision-based robotic
m...
Recent work in visual end-to-end learning for robotics has shown the pro...
Robotic skills can be learned via imitation learning (IL) using user-pro...
The success of deep reinforcement learning (RL) and imitation learning (...
Meta-learning algorithms aim to learn two components: a model that predi...
We study reinforcement learning in settings where sampling an action fro...
While robot learning has demonstrated promising results for enabling rob...
Imitation learning allows agents to learn complex behaviors from
demonst...
Well structured visual representations can make robot learning faster an...
Deep generative models are capable of learning probability distributions...
In this paper, we study the problem of learning vision-based dynamic
man...
In this paper, we explore deep reinforcement learning algorithms for
vis...
Humans are remarkably proficient at controlling their limbs and tools fr...
We consider the task of semantic robotic grasping, in which a robot pick...
Categorical variables are a natural choice for representing discrete
str...