Projects 2009/10 Giuseppina Gini - home.deib.polimi.ithome.deib.polimi.it/gini/AIR09.pdfCoordinate hand and arm movements: grasping, ... Haptic glove 20 What we have ... The goal of

RoboticsRobotics & AI & AI LabLab coursecourseProjects 2009/10

Giuseppina Gini

Prof. GiniDEI – Robotics

2 broad areas of research:

Robotics –bioinspired and autonomous

e-science and bioinformatics


3Bioinspired robotics

Imitation from nature:

structure

functions

behavior

Furby toy


4TOPIC 1: Intelligent manipulation

Very difficult problemMost of the cortex in humans is devoted to manipulation- Integration with vision and force sensors- Grasping strategies


5Available hardware

Maximum One (2003)

WhiteFingers (2002)

wrist (2009)

neck (2009)


6

Now:

• 7 GDL

• 12 actuators

• 0.5 Kg

• load: 1Kg

Tomorrow:

1. Complete hand actuation

2. New head with 2 moveable cameras

3. New FPGA controller

Additions (hw)


7

Now:

• Matlab/simulink controller

• Reflex control

• Simulation of V1 in visual cortex(disparity)

Tomorrow:

1. Coordinate hand and armmovements: grasping, ontology formanipulation; neural basis forgrasping.

2. Exploit the model of V1; create a benchmark for vision;

3. Simulate the visual cortex V2 (shapes and objects)

Additions (sw)


8Walking, swimming, and running robots

Legs

CPG

Dynamics

Interface


9Legged robots in lab

Ulisse (1994)LARP (2003)

ASGARD(2004)WARUGADAR(2008)

EMBOT (2008)


10LARP: (Light Adaptive Reactive biPed)

Height:1m

Weight:5Kg

Total active DOF: 12

- 3 in the hip

- 1 in the knee

- 2 in the ankle

Total passive DOF:4

- 2 in the footTo do:

Extending the simulator

Compute energy consumption

Walk


11warugadar

ADD:

- integration withexternal sensors

-Walking up and down hill

-CPG controller

•12 GDL

•ServoMotors

•Micro-controller on board


12EMBOT

4 legged running robot

ADD

Complete and experiment


13Fish

Zoidberg, 2008- EAP actuated

ADDAutonomous control


14Bioloid: mobile manipulation challenge

Mobile manipulation challenge at ICRA 2010

- Cleaning a room- Loading a dishwasher- Playing board games

- Experiment with differentarchitectures


Lego: emergent behaviors

Build robot colonies

(6 robot at max)

Use Braitemberg theory


16Mirror neurons and imitation learning


17Imitation of arm movements

Theory of imitation and modelling

Experiment


18Robot as interface

Hand prosthesis

Exoscheleton for rehabilitation

Haptic interface


19Robotic prosthesis

0 2000 4000 6000 8000 10000 12000 14000 16000-1

-0.8

-0.6

-0.4

-0.2

0

0.2

0.4

0.6

0.8

1

Wavelet ANN

Available

EMG acquisition

Classifier

To do

Controller

velocity/force

analysis


20Haptic glove

WhatWhat wewe havehave

--anan interface interface toto

capturecapture position and position and forceforce datadata

WhatWhat toto dodo

-- hardware hardware forfor forceforce detectiondetection

--learninglearning systemsystem

-- visualizationvisualization systemsystem


21Other sensing: e-nose

He_knows (multisensor, NN)

To do

recognition software

New hardware


22New interfaces: PyRo

Python Robotics. The goal of the project is to provide a programming environment

for easily exploring advanced topics in artificial intelligence and robotics without having to worry about the low-leveldetails of the underlying hardware.

NEW:Contribute new interfaces to our robots


23OOPS

TO DO

Develop an application

for

humanoids and

quadrupeds


24Project list

HUMANOIDSBIOINSPIRED ROBOT HEAD FOR VISION

NEW HARDWARE FOR MAXIMUMOne

SIMULATOR OF HUMANOID ROBOT.

INTEGRATING MANIPULATION AND VISION

MANIPULATION ONTOLOGIES

PATH PLANNING AND COLLISION AVOIDANCE IN OOPS

ROUTER WIRELESS FOR ROBOT CONTROL

WALKING ROBOTSKINEMATIC/DYNAMIC MODEL OF WARUGADAR

EMBOT WALKING

EXPERIMENTS WITH LEGO

EXPERIMENTS WITH BIOLOID

GAIT GENERATION AND CONTROL FOR WARUGADAR

ROBOFISH

EXOSCHELETON AND PROSTHESISADVANCED EMG ANALYSIS

WHITEFINGERS AS AN HAND PROSTHESIS

HAND PROSTHESIS USING ROBOTICS PRINCIPLES

SENSORS AND VISIONNEURAL-BASED VISION SYSTEM

RECOGNITION OF HAND MOTIONS

SEMANTIC MODELLING OF ACTIONS


252- E-sciencee-Science is the road to develop science

through distributed global collaborations enabled by the Internet.

- - it will require access to very large data collections, very large scale computing resources.

- - unseen correlation between such large data would be automatically detected by data mining and inductive systems.

“Now, with the human genetic code at last published and loadedonto CD-ROMs and DVDs, scientists are talking about a newera of medicine in which medical discoveries will be made not'in vivo' (in life) or 'in vitro' (in test tubes), but 'in silico,' or on computers."

—Rick Weiss


26Environment - health

Only 5% of availablechemicals have

biological effect data

In EU about 120000 chemicals to be

assessed in REACH

In vivo –millions of animals

In vitro – difficult relation with in vivo

In- silico

REACH

Registered chemicals: 28 millions


27The information side

Representation

Fingerprint (fragmentcount)Structural alerts(isomorphism)

Ligand (path planning)


The elements for knowledge discovery

Bioinformaticscan link resources and reveal known/unknown information about gene

and proteins, their relationships to biological functions and diseases.

Data Miningcan identify the therapeutically interesting targets present in the huge

corpus of knowledge.Molecular modelingallows exploration of target candidatesDockingplaces ligands in target active siteQSARcompares compounds activities to their structures


Virtual lab

ModellingModellingAgentAgent

RecallingRecalling--ModelModelAgentAgent

Experimental Planning AgentExperimental Planning Agent

Prediction AgentPrediction Agent

USERUSERInterfaceInterface

Interfacing AgentInterfacing Agent

Biological lab

ChemicalChemicalandand

BiologicalBiologicalLibraryLibrary

DB


30The target: biological modelling

Modelling the cellular interactions – the net of communications iscrucial

chemical cell tissue organ body

OPEN THE BOX


31Main tools used

OPEN SOURCE1. WEKA

(Waikato Environment for Knowledge Analysis) http://www.cs.waikato.ac.nz/~ml/weka

2. In house NN

Computational Chemistry tools1. CDK; 1. The Chemical Development Kit (CDK) is a Java-Library aiming at

providing all the basic classes and tools for chemical software under the GNU General Public License.

2. EPA tools


32In house development

1. Non parametric modelling: poliGMDHTheorem of Lorentz (1966) - any multivariate function can be

approximated to an arbitrary accuracy by a (particular) compositional network of univariate functions

2. Cascade-Correlation model: IReNNsThe structure of the network is defined during training.


33… but models are not theories

How models relate to theories?Actual models are neither derived entirely from data nor fromtheory.

1. We can use models when theory is not available2. Models are preliminary theories3. Models are a way to find out what are the causal relationships that

hold between certain facts and processes4. Causal reasoning is a powerful way of thinking

A theory of causality - J. PearlBeyond animal models

project: ORCHESTRAScientific communication--- teaching .


34Projects

DATA MINING FOR RULE INDUCTION

IReNNS: LEARNING FROM STRUCTURES

REC analysis in WEKA

HOW TO TEACH SCIENCE

CAUSAL REASONING


35Questions?

http://http://home.dei.polimi.ithome.dei.polimi.it//ginigini/Projects09.pdf/Projects09.pdf

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Projects 2009/10 Giuseppina Gini - home.deib.polimi.ithome.deib.polimi.it/gini/AIR09.pdfCoordinate hand and arm movements: grasping, ... Haptic glove 20 What we have ... The goal of