A key stumbling block for neural cross-language information retrieval (C...
Enabling robots to learn novel visuomotor skills in a data-efficient man...
Recent work in visual representation learning for robotics demonstrates ...
A popular approach to creating a zero-shot cross-language retrieval mode...
Humans are capable of completing a range of challenging manipulation tas...
Pretrained language models have improved effectiveness on numerous tasks...
The advent of transformer-based models such as BERT has led to the rise ...
Safe exploration is critical for using reinforcement learning (RL) in
ri...
Two key assumptions shape the usual view of ranked retrieval: (1) that t...
In this paper, we study the problem of learning a repertoire of low-leve...
We study the problem of learning a range of vision-based manipulation ta...
An agent that is capable of predicting what happens next can perform a
v...
We are motivated by the goal of generalist robots that can complete a wi...
A video prediction model that generalizes to diverse scenes would enable...
A generalist robot must be able to complete a variety of tasks in its
en...
Safety remains a central obstacle preventing widespread use of RL in the...
Learning from diverse offline datasets is a promising path towards learn...
Learned dynamics models combined with both planning and policy learning
...
Robot learning has emerged as a promising tool for taming the complexity...
Causal reasoning has been an indispensable capability for humans and oth...
Video prediction models combined with planning algorithms have shown pro...
A longstanding challenge in robot learning for manipulation tasks has be...
Our goal is for a robot to execute a previously unseen task based on a s...
In this work, we propose a novel robot learning framework called Neural ...
The introduction of autonomous vehicles (AVs) will have far-reaching eff...