Reinforcement Learning for Vision-based Object Manipulation with Non-parametric Policy and Action Primitives

TL;DR

This paper introduces AP-NPQL, a novel reinforcement learning framework utilizing non-parametric policies and action primitives, which improves efficiency and success rates in vision-based object manipulation tasks and transfers well to real robots.

Contribution

The paper presents a new non-parametric Q-learning approach with action primitives for efficient visual object manipulation, outperforming existing parametric methods.

Findings

Outperforms state-of-the-art algorithms in simulation tasks.

Achieves higher learning efficiency and success rates.

Successfully transfers policies from simulation to real robots.

Abstract

The object manipulation is a crucial ability for a service robot, but it is hard to solve with reinforcement learning due to some reasons such as sample efficiency. In this paper, to tackle this object manipulation, we propose a novel framework, AP-NPQL (Non-Parametric Q Learning with Action Primitives), that can efficiently solve the object manipulation with visual input and sparse reward, by utilizing a non-parametric policy for reinforcement learning and appropriate behavior prior for the object manipulation. We evaluate the efficiency and the performance of the proposed AP-NPQL for four object manipulation tasks on simulation (pushing plate, stacking box, flipping cup, and picking and placing plate), and it turns out that our AP-NPQL outperforms the state-of-the-art algorithms based on parametric policy and behavior prior in terms of learning time and task success rate. We also successfully transfer and validate the learned policy of the plate pick-and-place task to the real robot in a sim-to-real manner.