A Review of Robot Learning for Manipulation: Challenges, Representations, and Algorithms

TL;DR

This paper surveys recent advances in robot learning for manipulation, emphasizing challenges, representations, and algorithms, and formalizes the problem to identify research gaps and future opportunities.

Contribution

It provides a comprehensive formalization of robot manipulation learning and synthesizes existing research to highlight key challenges and future directions.

Findings

Growth in machine learning approaches for robot manipulation

Identification of key challenges in autonomous manipulation

Framework for analyzing manipulation learning research

Abstract

A key challenge in intelligent robotics is creating robots that are capable of directly interacting with the world around them to achieve their goals. The last decade has seen substantial growth in research on the problem of robot manipulation, which aims to exploit the increasing availability of affordable robot arms and grippers to create robots capable of directly interacting with the world to achieve their goals. Learning will be central to such autonomous systems, as the real world contains too much variation for a robot to expect to have an accurate model of its environment, the objects in it, or the skills required to manipulate them, in advance. We aim to survey a representative subset of that research which uses machine learning for manipulation. We describe a formalization of the robot manipulation learning problem that synthesizes existing research into a single coherent framework and highlight the many remaining research opportunities and challenges.