We introduce Masked Trajectory Models (MTM) as a generic abstraction for...
We present the largest and most comprehensive empirical study of pre-tra...
We introduce Contrastive Intrinsic Control (CIC), an algorithm for
unsup...
Reward function specification, which requires considerable human effort ...
Offline Reinforcement Learning (RL) aims to extract near-optimal policie...
We present a framework that abstracts Reinforcement Learning (RL) as a
s...
Model-based algorithms, which learn a dynamics model from logged experie...
Offline reinforcement learning (RL) refers to the problem of learning
po...
In offline reinforcement learning (RL), the goal is to learn a successfu...
Model-based reinforcement learning (MBRL) has recently gained immense
in...
We introduce Lyceum, a high-performance computational ecosystem for robo...
A core capability of intelligent systems is the ability to quickly learn...
A central capability of intelligent systems is the ability to continuous...
We propose a plan online and learn offline (POLO) framework for the sett...
Dexterous multi-fingered robotic hands can perform a wide range of
manip...
Reinforcement learning has emerged as a promising methodology for traini...
Policy gradient methods have enjoyed great success in deep reinforcement...
Standard model-free deep reinforcement learning (RL) algorithms sample a...
Dexterous multi-fingered hands are extremely versatile and provide a gen...
Sample complexity and safety are major challenges when learning policies...
Consumers with low demand, like households, are generally supplied
singl...