Realistic manipulation tasks require a robot to interact with an environment with a prolonged sequence of motor actions. While deep reinforcement learning methods have recently emerged as a promising paradigm for automating manipulation behaviors, they usually fall short in long-horizon tasks due to the exploration burden. This work introduces MAnipulation Primitive-augmented reinforcement LEarning (MAPLE), a learning framework that augments standard reinforcement learning algorithms with a pre-defined library of behavior primitives. These behavior primitives are robust functional modules specialized in achieving manipulation goals, such as grasping and pushing. To use these heterogeneous primitives, we develop a hierarchical policy that involves the primitives and instantiates their executions with input parameters. We demonstrate that MAPLE outperforms baseline approaches by a significant margin on a suite of simulated manipulation tasks. We also quantify the compositional structure of the learned behaviors and highlight our method's ability to transfer policies to new task variants and to physical hardware. Videos and code are available at https://ut-austin-rpl.github.io/maple

本文介绍一种基于预定义行为模块的学习框架Manipulation Primitive-Augmented Reinforcement Learning（MAPLE），通过这种学习框架实现机器人在多种实际操作任务中表现出色，MAPLE 具有超越基线方法的性能。

利用行为基元增强强化学习，以实现多样化的操作任务