AI planning approaches to robotics Jeremy Wyatt School of Computer Science University of Birmingham

AI planning approaches to robotics

Jeremy Wyatt

School of Computer Science

University of Birmingham

Early models of intelligence

• Perceive-think-act model of intelligence(Kenneth Craik, 1943)

• This model was very influential in early AI

Perceive Think Act

Perceive Think Act for robotics

• By the 1960’s we had– Simple vision systems– Simple theorem provers (using resolution)– Simple path planning methods

• Idea: put them all together in a robotSHAKEY Project

Shakey the robot

• 1970-Shakey the robot reasons about its blocksBuilt at Stanford Research Institute, Shakey was remote controlled by a large computer. It hosted a clever reasoning program fed very selective spatial data, derived from weak edge-based processing of camera and laser range measurements. On a very good day it could formulate and execute, over a period of hours, plans involving moving from place to place and pushing blocks to achieve a goal.

– From Hans Moravec

Shakey outline

Planex

Strips

ILAs

LLAs

Hardware

World

Model

• central representation

• logic based

• error recovery at several levels

• communication through model

Shakey: key ingredients

• Geometric planning within ILAs to avoid obstacles, eg. goto(d4)

• ILAs did simple error recovery (reactive controllers)e.g. push(box1, (14.1 22.3))

• Major error recovery done by updating the world modele.g. if the robot is uncertain about its position it takes a camera fix and updates the world model.

• World model based on First Order Predicate Logic (FOPL)

Shakey: key ingredients

• World model used logical representationstype(r1,room)

in(shakey,r1)

in(o1,r2)

type(d1 door)

type(o1 object)

type(f3 face)

type(shakey)

at(o1 15.1 21.0)

joinsfaces(d2 f3 f4)

joinsrooms(d2 r3 r2)

…

shakey

30

20

10

0

0 10 20

r3

f4 f3

d2

d1

f2

f1

r1

r2

o1

Shakey: key ingredients

• Planner used specialised representations to be faster, e.g. actions represented using STRIPS operatorsblock_door(D,Y)

preconditions: in(shakey,X) & in(Y,X)

& clear(D) & door(D)

& object(Y)

delete list: clear(D)

add list: blocked(D,Y)

Planning• Shakey used a form of planning called goal regression

• Idea: find an action that directly achieves your goal, and then actions to achieve the first action’s preconditions, etc…

• e.g. Blocked(d1,X)

block_door(D,Y)preconditions: in(shakey,X) & in(Y,X)

& clear(D) & door(D)& object(Y)

delete list: clear(D)add list: blocked(D,Y)

shakey

30

20

10

0

0 10 20

r3

f4 f3

d2

d1

f2

f1

r1

r2

o1

Planning

• Shakey could learn to chunk useful sequences of actions into single large actions called macrops

• But STRIPS was slow and weak

• Sussman anomaly

After Shakey

• Shakey looked promising

• But it worked in a very

restricted environment

• Could it be extended to

natural worlds?

Stanford Cart, 1970s

After Shakey

• After twenty years the approach still didn’t extend– Visual modelling too hard and slow– Non-linear planning intractable (NP-complete)– Feedback through world model cumbersome

• People began to wonder if the ideas were right

Reading

Russell and Norvig, Chapter 11 (Planning)

Shakey the Robot, Technical report (in school library)