OHPL: One-shot Hand-eye Policy Learner

TL;DR

This paper introduces OHPL, a one-shot learning approach for robot reaching tasks using a hand-eye camera, leveraging view change analogy and reinforcement learning to reduce training data needs.

Contribution

It proposes a novel one-shot policy learning method from a single image using view change analogy and a dynamic filter for better adaptation.

Findings

Effective in static object reaching tasks

Successfully applied to moving object reaching

Reduces training data and time required

Abstract

The control of a robot for manipulation tasks generally relies on object detection and pose estimation. An attractive alternative is to learn control policies directly from raw input data. However, this approach is time-consuming and expensive since learning the policy requires many trials with robot actions in the physical environment. To reduce the training cost, the policy can be learned in simulation with a large set of synthetic images. The limit of this approach is the domain gap between the simulation and the robot workspace. In this paper, we propose to learn a policy for robot reaching movements from a single image captured directly in the robot workspace from a camera placed on the end-effector (a hand-eye camera). The idea behind the proposed policy learner is that view changes seen from the hand-eye camera produced by actions in the robot workspace are analogous to locating a region-of-interest in a single image by performing sequential object localisation. This similar view change enables training of object reaching policies using reinforcement-learning-based sequential object localisation. To facilitate the adaptation of the policy to view changes in the robot workspace, we further present a dynamic filter that learns to bias an input state to remove irrelevant information for an action decision. The proposed policy learner can be used as a powerful representation for robotic tasks, and we validate it on static and moving object reaching tasks.