Learning Visual Affordances with Target-Orientated Deep Q-Network to Grasp Objects by Harnessing Environmental Fixtures

Understand

This paper introduces a challenging object grasping task and proposes a self-supervised learning approach.

• The goal of the task is to grasp an object which is not feasible with a single parallel gripper, but only with harnessing environment fixtures (e.g., walls, furniture, heavy objects).
• This Slide-to-Wall grasping task assumes no prior knowledge except the partial observation of a target object.
• Hence the robot should learn an effective policy given a scene observation that may include the target object, environmental fixtures, and any other disturbing objects.