General-purpose robotic systems must master a large repertoire of diverse skills. While
reinforcement learning provides a powerful framework for acquiring individual behaviors, the …

A key challenge for robotic manipulation in open domains is how to acquire diverse and
generalizable skills for robots. Recent progress in one-shot imitation learning and robotic …

With the advent of large language models and large-scale robotic datasets, there has been
tremendous progress in high-level decision-making for object manipulation. These generic …

This paper introduces PyRobot, an open-source robotics framework for research and
benchmarking. PyRobot is a light-weight, high-level interface on top of ROS that provides a …

Abstract We present Skill Transformer, an approach for solving long-horizon robotic tasks by
combining conditional sequence modeling and skill modularity. Conditioned on egocentric …

Learning dexterous manipulation in high-dimensional state-action spaces is an important
open challenge with exploration presenting a major bottleneck. Although in many cases the …

Recent work has demonstrated the ability of deep reinforcement learning (RL) algorithms to
learn complex robotic behaviours in simulation, including in the domain of multi-fingered …

This paper introduces Amazon Robotic Manipulation Benchmark (ARMBench), a large-
scale, object-centric benchmark dataset for robotic manipulation in the context of a …

Reinforcement learning (RL) has been shown to be effective at learning control from
experience. However, RL typically requires a large amount of online interaction with the …

In multigoal reinforcement learning (RL), algorithms usually suffer from inefficiency in the
collection of successful experiences in tasks with sparse rewards. By utilizing the ideas of …