In this work, we present a scalable reinforcement learning method for
tr...
We study how vision-language models trained on Internet-scale data can b...
We describe a system for deep reinforcement learning of robotic manipula...
Large language models excel at a wide range of complex tasks. However,
e...
For robots to follow instructions from people, they must be able to conn...
Recent progress in large language models (LLMs) has demonstrated the abi...
Recent advances in robot learning have shown promise in enabling robots ...
By transferring knowledge from large, diverse, task-agnostic datasets, m...
In recent years, much progress has been made in learning robotic manipul...
Large language models (LLMs) trained on code completion have been shown ...
Leveraging many sources of offline robot data requires grappling with th...
Recent works have shown how the reasoning capabilities of Large Language...
Reinforcement learning (RL) provides a theoretical framework for continu...
Large language models can encode a wealth of semantic knowledge about th...
Reinforcement learning systems have the potential to enable continuous
i...
Offline reinforcement learning (RL) can learn control policies from stat...
Reinforcement learning (RL) provides a naturalistic framing for learning...
Robotic skills can be learned via imitation learning (IL) using user-pro...
Offline reinforcement learning (RL) algorithms have shown promising resu...
Reinforcement learning (RL) promises to enable autonomous acquisition of...
General-purpose robotic systems must master a large repertoire of divers...
We consider the problem of learning useful robotic skills from previousl...
One of the most challenging aspects of real-world reinforcement learning...
Representing the environment is a fundamental task in enabling robots to...
Complex object manipulation tasks often span over long sequences of
oper...
Reinforcement learning provides a general framework for learning robotic...
One of the great promises of robot learning systems is that they will be...
We study reinforcement learning in settings where sampling an action fro...
While deep learning and deep reinforcement learning (RL) systems have
de...
We present relay policy learning, a method for imitation and reinforceme...
Meta-reinforcement learning algorithms can enable robots to acquire new
...
The distributional perspective on reinforcement learning (RL) has given ...
Conventionally, model-based reinforcement learning (MBRL) aims to learn ...
When deploying autonomous agents in the real world, we need to think abo...
Developing agents that can quickly adapt their behavior to new tasks rem...
Simulation-to-real transfer is an important strategy for making reinforc...
We present a novel solution to the problem of simulation-to-real transfe...
Learning a policy capable of moving an agent between any two states in t...