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A Software Architecture and Tools for Autonomous Robots that Learn on Mission

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Title: A Software Architecture and Tools for Autonomous Robots that Learn on Mission


1
A Software Architecture and Tools for Autonomous
Robots that Learn on Mission
K. Kawamura, M. Wilkes, A. Peters, D.
Gaines Vanderbilt UniversityCenter for
Intelligent Systems Jet Propulsion
Laboratory http//shogun.vuse.vanderbilt.edu/cis/D
ARPA/
February 2002 MARS PI Meeting
2
Objective
  • Develop a multi-agent based robot control
    architecture for humanoid and mobile robots that
    can
  • accept high-level commands from a human
  • learn from experience to modify existing
    behaviors, and
  • share knowledge with other robots

3
Accomplishments
  1. Multi-agent based robot control architectures for
    humanoid and mobile robots have been developed
  2. Agent-based Human-Robot Interfaces have been
    developed for humanoid and mobile robots
  3. SES (Sensory EgoSphere), a short-term robot
    memory, was developed and transferred to NASA/JSC
    Robonaut group
  4. SES- LES (Landmark EgoSphere)- based navigation
    algorithm was developed and tested
  5. SES knowledge sharing among mobile robots was
    developed and tested
  6. SAN-RL (Spreading Activation Network -
    Reinforcement Learning) method was applied to
    mobile robots for dynamic path planning

4
Presentation / Demo
  • Multi-agent based Robot Control Architecture
  • Humanoid
  • Mobile robots
  • Agent-based Human Robot Interfaces
  • Humanoid (face-to-face)
  • Mobile robots (GUI)
  • Sensory EgoSphere (SES)
  • Humanoid
  • Mobile robots
  • SES and LES based Navigation
  • SES and LES Knowledge Sharing
  • Dynamic Path Planing through SAN-RL

5
Multi-Agent Based Robot Control Architecture for
Humanoids
Novel Approach Distributed architecture that
expressly represents human and humanoid internally
Publication 1,2
6
Multi-Agent Based Robot Control Architecture for
Mobile Robots
Novel Approach Distributed, agent-based
architecture to gather mission relevant
information from robots
Publication 7
7
Agent-based Human-Robot Interfaces for Humanoids
  • Self Agent (SA)
  • monitors humanoids activity and performance for
    self-awareness and reporting to human
  • determines the humanoids intention and response
    and reports to human
  • Human Agent (HA)
  • observes and monitors the communications and
    actions of people
  • extracts persons intention for interaction
  • communicates with people

Novel Approach Modeling the humans and
humanoids intent for interaction
Publication 3,4,5
8
Agent-based Human-Robot Interface for Mobile
Robots
Camera UIC
Sonar UIC
Novel Approach Interface that adapts to the
current context of the mission in addition to
user preferences by using User Interface
Components (UIC) and an agent-based architecture
Publication 7
9
Sensory EgoSphere (SES) for Humanoids
  • Objects in ISACs immediate environment are
    detected
  • Objects are registered onto the SES at the
    interface nodes
    closest to the objects perceived locations
  • Information about a sensory object is stored in a
    database with the node location and other index

Publication 2
10
Sensory EgoSphere Display for Humanoids
Provides a tool for person to visualize what
ISAC has detected
11
Sensory EgoSphere (SES) for Mobile Robots
  • The SES can be used to enhance a graphical user
    interface and to increase situational awareness
  • In a GUI, the SES translates mobile robot
    sensory data from the sensing level to the
    perception level in a compact form
  • The SES is also used for perception-based
    navigation with a Landmark EgoSphere
  • The can be also used for supervisory control of
    mobile robots
  • Perceptual and sensory information is mapped on a
    geodesically tessellated sphere
  • Distance information is not explicitly
    represented on SES
  • A sequence of SESs will be stored in the database

SES
2d EgoCentric view
Top view
Publication 6
12
SES- and LES-Based Navigation
  • Navigation based on EgoCentric representations
  • SES represents the current perception of the
    robot
  • LES represents the expected state of the world
  • Comparison of these provide the best estimate
    direction towards a desired region
  • more

Future Work
Novel Approach Range-free perception-based
navigation
Publication 8
13
SES and LES Knowledge Sharing
  • Skeeter creates SES
  • Skeeter finds the object
  • Skeeter shares SES data with Scooter
  • Scooter calculates heading to the object
  • Scooter finds the object
  • Scooter has the map of the environment
  • Scooter generates via LESs
  • Scooter shares LES data with Skeeter
  • Skeeter navigates to the target using PBN

Novel Approach A team of robots that share SES
and LES knowledge
Future Work
Publication 9
14
Dynamic Path Planning through SAN-RL(Spreading
Activation Network - Reinforcement Learning)
High level command with multiple goals
  • Behavior Priority
  • Using the shortest time
  • Avoid enemy
  • Equal priority
  • More

Get initial data from learning mode
After finish training send data back to DB
SAN-RL
activate/deactivate robots behaviors
Scooter
Atomic Agents
Novel Approach Action selection with learning
for the mobile robot
Publication 10
15
Publications
  • K. Kawamura, R.A. Peters II, D.M. Wilkes, W.A.
    Alford, and T.E. Rogers, "ISAC Foundations in
    Human-Humanoid Interaction", IEEE Intelligent
    Systems, July/August 2000.
  • K. Kawamura, A. Alford, K. Hambuchen, and M.
    Wilkes, "Towards a Unified Framework for
    Human-Humanoid Interaction", Proceedings of the
    First IEEE-RAS International Conference on
    Humanoid Robots, September 2000.
  • K. Kawamura, T.E. Rogers and X. Ao, Development
    of a Human Agent for a Multi-Agent Based
    Human-Robot Interaction, Submitted to First
    International Joint Conference on Autonomous
    Agents and Multi-Agent Systems (AAMAS 2002),
    Bologna, Italy, July 15-19, 2002.
  • T. Rogers, and M. Wilkes, "The Human Agent a
    work in progress toward human-humanoid
    interaction" Proceedings 2000 IEEE International
    Conference on Systems, Man and Cybernetics,
    Nashville, October, 2000.
  • A. Alford, M. Wilkes, and K. Kawamura, "System
    Status Evaluation Monitoring the state of agents
    in a humanoid system, Proceedings 2000 IEEE
    International Conference on Systems, Man and
    Cybernetics, Nashville, October, 2000.
  • K. Kawamura, R. A. Peters II, C. Johnson, P.
    Nilas, S. Thongchai, Supervisory Control of
    Mobile Robots Using Sensory EgoSphere, IEEE
    International Symposium on Computational
    Intelligence in Robotics and Automation, Banff,
    Canada, July 2001.
  • K. Kawamura, D.M. Wilkes, S. Suksakulchai, A.
    Bijayendrayodhin, and K. Kusumalnukool,
    Agent-Based Control and Communication of a Robot
    Convoy, Proceedings of the 5th International
    Conference on Mechatronics Technology, Singapore,
    June 2001.
  • K. Kawamura, R.A. Peters II, D.M. Wilkes, A.B.
    Koku and A. Sekman, Towards Perception-Based
    Navigation using EgoSphere, Proceedings of the
    International Society of Optical Engineering
    Conference (SPIE), October 28-20, 2001.
  • K. Kawamura, D.M. Wilkes, A.B. Koku, T.
    Keskinpala, Perception-Based Navigation for
    Mobile Robots, accepted Proceedings of
    Multi-Robot System Workshop, Washington, DC,
    March 18-20, 2002.
  • D.M. Gaines, M. Wilkes, K. Kusumalnukool, S.
    Thongchai, K. Kawamura and J. White, SAN-RL
    Combining Spreading Activation Networks with
    Reinforcement Learning to Learn Configurable
    Behaviors, Proceedings of the International
    Society of Optical Engineering Conference (SPIE),
    October 28-20, 2001.

16
Acknowledgements
  • This work has been partially sponsored under the
  • DARPA MARS Grant DASG60-99-1-0005
  • and from the
  • NASA/JSC - UH/RICIS Subcontract NCC9-309-HQ
  • Additionally, we would like to thank the
    following CIS students
  • Mobile Robot Group Bugra Koku, Carlotta Johnson,
    Turker Keskinpala, Anak Bijayendrayodhin, Kanok
    Kusumalnukool, Jian Peng
  • Humanoid Robotic GroupTamara Rogers, Kim
    Hambuchen, Christina Campbell
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