REBOOT: Reuse Data for Bootstrapping Efficient Real-World Dexterous
  Manipulation
Zheyuan Hu Aaron Rovinsky Jianlan Luo Vikash Kumar Abhishek Gupta Sergey Levine
Dexterous manipulation tasks involving contact-rich interactions pose a
significant challenge for both model-based control systems and imitation
learning algorithms. The complexity arises from the need for multi-fingered
robotic hands to dynamically establish and break contacts, balance
non-prehensile forces, and control large degrees of freedom. Reinforcement
learning (RL) offers a promising approach due to its general applicability and
capacity to autonomously acquire optimal manipulation strategies. However, its
real-world application is often hindered by the necessity to generate a large
number of samples, reset the environment, and obtain reward signals. In this
work, we introduce an efficient system for learning dexterous manipulation
skills with RL to alleviate these challenges. The main idea of our approach is
the integration of recent advances in sample-efficient RL and replay buffer
bootstrapping. This combination allows us to utilize data from different tasks
or objects as a starting point for training new tasks, significantly improving
learning efficiency. Additionally, our system completes the real-world training
cycle by incorporating learned resets via an imitation-based pickup policy as
well as learned reward functions, eliminating the need for manual resets and
reward engineering. 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. (Videos: https://sites.google.com/view/reboot-dexterous)