Reset-Free Reinforcement Learning via Multi-Task Learning: Learning Dexterous Manipulation Behaviors without Human Intervention

no code implementations • 22 Apr 2021 • Abhishek Gupta, Justin Yu, Tony Z. Zhao, Vikash Kumar, Aaron Rovinsky, Kelvin Xu, Thomas Devlin, Sergey Levine

This work shows the ability to learn dexterous manipulation behaviors in the real world with RL without any human intervention.

Multi-Task Learning Reinforcement Learning (RL)

Dexterous Manipulation from Images: Autonomous Real-World RL via Substep Guidance

no code implementations • 19 Dec 2022 • Kelvin Xu, Zheyuan Hu, Ria Doshi, Aaron Rovinsky, Vikash Kumar, Abhishek Gupta, Sergey Levine

In this paper, we describe a system for vision-based dexterous manipulation that provides a "programming-free" approach for users to define new tasks and enable robots with complex multi-fingered hands to learn to perform them through interaction.

reinforcement-learning Reinforcement Learning (RL)

REBOOT: Reuse Data for Bootstrapping Efficient Real-World Dexterous Manipulation

no code implementations • 6 Sep 2023 • Zheyuan Hu, Aaron Rovinsky, Jianlan Luo, Vikash Kumar, Abhishek Gupta, Sergey Levine

We demonstrate the benefits of reusing past data as replay buffer initialization for new tasks, for instance, the fast acquisition of intricate manipulation skills in the real world on a four-fingered robotic hand.

Imitation Learning Reinforcement Learning (RL)