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)