Cognitive Colonization

Tony Stentz, Martial Hebert,

Bruce Digney, Scott Thayer

Robotics Institute

Carnegie Mellon University

Requirements

Distributed robotics for small-scale mobile robots calls for a software system that:

• is robust to individual robot failure;

• does not depend on reliable communications;

• can perform global tasks given the limited sensing and computational capabilities of individual robots;

• learn to perform better through experience.

Cognitive Colonization Paradigm

The proposed software system addresses these requirements by:

• dynamically assigning robots to tasks and checkpointing data;

• treating communication as an opportunistic resource;

• aggregating resources by distributing the computational and perceptual load across the group of robots;

• sharing learned behaviors (both individual and group) between all robots.

Software Architecture

Command Unit

MobileRobot

MobileRobot Mobile

Robot

MobileRobot

MobileRobot

MobileRobot

Squad Level

MobileRobot

MobileRobot

MobileRobot

Squad Level

MobileRobot

MobileRobot

MobileRobot

MobileRobot

Example: Distributed Mapping

Unattached Robot

SingleRobot

Command Unit

Mapping Squad

Mapping Squad

Communications Squad

Colony Level

Objectives:

Adaptations:

Planning:

Data Acquired:

Subordinates:

Behaviors:

- directives

- positions- health

- world map

- merge- split- grow- regions to map- comm areas to maintain

- all known behaviors

- aggregate adapted behaviors

Squad Level

Objectives:

Adaptations:

Planning:

Data Acquired:

Subordinates:

Behaviors:

- map a portion- commlink

- positions- data quality- comm quality

- local map

- more comm squads- positions- fields of view

- maintain internal comm- maintain external comm- re-establish comm- map a region

- better mapping strategies- better comm strategies

Robot Level

Objectives:

Adaptations:

Planning:

Data Acquired:

Subordinates:

Behaviors:

- take sensor data- relay data

- position- health

- sensor data- comm data

- safeguarding- maintain comm- re-establish comm- bootstrap colony

- better nav- better comm

Deliverables

• Reactive behaviors

• Communications protocols and strategies

• Resource allocation strategies

• Learning algorithms

• Planning algorithms

• Demonstration systems

Schedule

Robust Colonization

Port to Military Platforms

ColonizationDynamics

2000 2001 2002 2003

StaticColonization

DARPA Demo II Project

GRAMMPS: Initial Plan

GRAMMPS: Swapping Goals

GRAMMPS: Plan Completed

Chornobyl Nuclear Power Plant

Pioneer Robot

3-D Map Data

New Ideas

• Distributed control architecture for formation of new robot colonies.

• Probabilistic model of robot existence.

• “In-situ” group learning, behavior exchange, skill swapping.

• Multiple colony dynamics.

Learning Hierarchical Control Structures

Primitive Actions

Sen

sors

an

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ein

forc

emen

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als

Flat Structure Hierarchical Structure

Learning Control System Learning Control System

Behavior BehaviorS

enso

rs a

nd

Rei

nfo

rcem

ent

Sig

nal

s

Primitive Actions

Behavior Behavior

Behavior Behavior

Behavior Behavior

Generating Lower SkillsNew Task

New Task

World Change

World Change

0

-50

-100

-150

-200

-250

-300

0

-50

-100

-150

-200

-250

-3000 50 100 150 200 250 300 350

0 50 100 150 200 250 300 350TIME

TIME

PE

RF

OR

MA

NC

EP

ER

FO

RM

AN

CE

HIERARCHICAL

FLAT

Benefits of Hierarchical Learning

• Decomposed behaviors transfer between tasks, environments and robots

• Confines disruptions to only levels affected

• Generates levels of abstraction

• Suitable for robot pretraining

Behavior Learning in Colonies

• Can use shared experiences/information to speed learning

• Applicable to both individual robots, squads and command units

• Suitable for exploration with robot death/sacrifice