Robot learning by demonstration

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Author: Dr. Aude Billard, EPFL Ecole Polytechnique Federale de Lausanne

Dr. Aude Billard accepted the invitation on 14 July 2007 (self-imposed deadline: 14 January 2008).

This article will briefly cover: Research on robot programming by demonstration from the standpoint of machine learning and computational neuroscience.

WORK IN PROGRESS!!

1 Introduction
- 1.1 Background and History
- 1.2 Interfaces
2 Background and History
3 Dynamical Systems's Approach
4 Symbolic Approach
5 Scaffolding, Moulding and Incremental Teaching
6 References
7 Links
8 Source Codes

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Introduction

Figure 1:

Robot Programming by demonstration (PbD) develops algorithms by which a human can teach a robot a task without explicitly programming the robot, but rather by simply "showing" to the robot how to do the task. The roobot learns from observing the human. There are three main problems in PbD:

The robot must Since robots and humans do not have the same body, the demonstrations

This resembles the way human learn. The method is thus deemed to provide a powerful way for lay people (workers in factories or house-keepers) to teach robots. PbD requires usually less than 10 demonstrations of the same task to make the training sessions bearable for the user.

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Background and History

Robot Programming by demonstration (PbD) has become a central topic of robotics that spans across general research areas such as human-robot interaction, machine learning, machine vision and motor control. Robot PbD started about 30 years ago, and has grown importantly during the past decade. The rationale for moving from purely preprogrammed robots to very flexible user-based interfaces for training robots to perform a task is three-fold.

First and foremost, PbD, also referred to as imitation learning, is a powerful mechanism for reducing the complexity of search spaces for learning. When observing either good or bad examples, one can reduce the search for a possible solution, by either starting the search from the observed good solution (local optima), or conversely,by eliminating from the search space what is known as a bad solution. Imitation learning is, thus, a powerful tool for enhancing and accelerating learning in both animals and artifacts.

Second, imitation learning offers an implicit means of training a machine, such that explicit and tedious programming of a task by a human user can be minimized or eliminated. Imitation learning is thus a "natural" means of interacting with a machine that would be accessible to lay people.

Third, studying and modeling the coupling of perception and action, which is at the core of imitation learning, helps us to understand the mechanisms by which the self-organization of perception and action could arise during development. The reciprocal interaction of perception and action could explain how competence in motor control can be grounded in rich structure of perceptual variables, and vice versa, how the processes of perception can develop as means to create successful actions.

PbD promises were thus multiple. On the one hand, one hoped that it would make learning faster, in contrast to tedious reinforcement learning methods or trials-and-error learning. On the other hand, one expected that the methods, being user-friendly, would enhance the application of robots in human daily environments.

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Interfaces

Several interfaces can be used to teach the robot:

Kinesthetic Teaching consists in guiding the robot through the task's steps by moving passively its limbs

Image:PbD-Kin.jpg

Figure 2:

Motion Sensors worn by the trainer allows the robot to track the motion of each of the user's limbs. The method

Image:PbD-MotionSensors.jpg

Figure 3:

using motion sensors and having the robot mirroring the motion on the fly.

Teaching can be either done in batch, i.e. by showing all the demonstrations at once and then get the robot to replicate a "generalized" version of the

to the robot that observes passively