SA-1

Robotic Self-Perception and Body Scheme Learning

Jürgen SturmChristian PlagemannWolfram Burgard

University of FreiburgGermany

Motivation

Existing robot models are typically specified (geometrically) in advance calibrated manually

Motivation

Problems with fixed robot models: Wear-and-tear

wheel diameter, air pressure

Recovery from failure malfunctioning actuators

Tool use extending the model

Unknown modelre-configurable robots

Problems with fixed robot models: Wear-and-tear

wheel diameter, air pressure

Recovery from failure malfunctioning actuators

Tool use extending the model

Unknown modelre-configurable robots

Similar problems in humans/animals?

Motivation

Problems with fixed robot models: Wear-and-tear

wheel diameter, air pressure

Recovery from failure malfunctioning actuators

Tool use extending the model

Unknown modelre-configurable robots

Similar problems in humans/animals?

Motivation

growth, aging

injured body parts

writing

riding a bike

Related Work

Neuro-physiology Mirror neurons [Rizzolatti et al., 1996]

Body Schemes [Maravita and Iriki, 2004]

Robotics Self-calibration [Roy and Thrun, 1999]

Cross-modal maps [Yoshikawa et al., 2004]

Structure learning [Dearden and Demiris, 2005]

Problem motivation

Fixed-model approaches fail when parameters change over time geometric model is not available

Bootstrapping of the body scheme and Life-long adaptation using visual

self-observation

Our Contribution

Sense6D Poses

ActJoint angles

ThinkBootstrap, monitor, and maintaininternal representation of body

Problem Description

Problem Formulation

Visual self-perception of n body parts:

Actuators (m action signals):

Learn the mapping

p(X 1; : : : ;X n ja1; : : : ;am)

X 1; : : : ;X n 2 R4£ 4

Body pose Configuration

a1; : : : ;am 2 R

Existing Methods

Analytic model + parameter estimation

Function approximation Nearest neighbor Neural networks

Requires prior knowledge

High-dimensional learning problem

Requires large training sets

Body Scheme Factorization

Idea: Factorize the model

We represent the kinematic chain as a Bayesian network

Bootstrapping

Learning the model from scratch consists of two steps:

1. Learning the local models (conditionaldensity functions)

2. Finding the network/body structure

Learning the Local Models

Using Gaussian process regression Learn 1D 6D transformation function

for each (action, marker, marker) triple

p(¢ 12 j a1) = p(X ¡ 11 X 2 j a1)

Finding the Network Structure

Select the most likely network topology

Corresponding to the minimum spanning tree

Maximizing the data likelihoodp(M jD)

Model Selection

Model Selection

7-DOF example

Fully connected BN

Model Selection7-DOF example

Fully connected BN

Selected minimalspanning tree

Forward Kinematics

Purpose: prediction of end-effector pose in a given

configuration Approach:

integrate over the kinematicchain in the Bayesian network

by concatenating Gaussians approximate the result

efficiently by one Gaussianp(X n jX 1;a1; : : : ;am) =Z

:::Z

pM 1pM 2

: : :dX 2; : : : ;dX n¡ 1

Inverse Kinematics

Purpose: Generate motor commands for reaching a given target pose

Approach: Estimate Jacobian of end-effector using forward kinematics prediction

Use standard IK techniques Jacobian pseudo-inverse

r Xn(a) =

·@X n(a)

@a1; : : : ;

@X n(a)@am

¸

Experiments

Evaluation: Forward Kinematics

Fast convergence (approx. 10-20 iterations) High accuracy (higher than direct perception)

Evaluation: Inverse Kinematics

Accurate control using bootstrapped body scheme

Life-long Adaptation

Robot’s physical properties will change over time

Predictive accuracy of body scheme needs to be monitored continuously

Localize mismatches in the Bayesian network Re-learn parts of the network

Life-long Adaptation

Initial

Error is detected and is localized

Robot re-learns some local models

Life-long Adaptation

Evaluation

Quick localization of error Robust recovery

Summary

Novel approach learning body schemes from scratch using visual self-perception Model learning using Gaussian process

regression Model selection using data likelihood as

criterion

Efficient adaptation to changes in robot geometry

Accurate prediction and control

Future Work

Active self-exploration, optimal control, POMDPs

Marker-less self-perception

Moving robot

Tool use