Q-attention: Enabling Efficient Learning for Vision-based Robotic Manipulation

TL;DR

This paper introduces ARM, an attention-driven algorithm that improves reinforcement learning for robotic manipulation tasks by focusing on relevant image regions, enabling efficient learning from limited demonstrations.

Contribution

The paper presents Q-attention, a novel attention mechanism that enhances RL-based robotic manipulation by focusing on key image regions, improving training efficiency and success rates.

Findings

ARM outperforms existing RL algorithms on RLBench tasks.

Q-attention effectively identifies relevant object regions from images.

The method requires fewer demonstrations for successful learning.

Abstract

Despite the success of reinforcement learning methods, they have yet to have their breakthrough moment when applied to a broad range of robotic manipulation tasks. This is partly due to the fact that reinforcement learning algorithms are notoriously difficult and time consuming to train, which is exacerbated when training from images rather than full-state inputs. As humans perform manipulation tasks, our eyes closely monitor every step of the process with our gaze focusing sequentially on the objects being manipulated. With this in mind, we present our Attention-driven Robotic Manipulation (ARM) algorithm, which is a general manipulation algorithm that can be applied to a range of sparse-rewarded tasks, given only a small number of demonstrations. ARM splits the complex task of manipulation into a 3 stage pipeline: (1) a Q-attention agent extracts relevant pixel locations from RGB and point cloud inputs, (2) a next-best pose agent that accepts crops from the Q-attention agent and outputs poses, and (3) a control agent that takes the goal pose and outputs joint actions. We show that current learning algorithms fail on a range of RLBench tasks, whilst ARM is successful.