Transcript Introduction - Computer Science and Engineering

Advanced Topics in Robotics
CS493/790 (X)
Lecture 1
Instructor: Monica Nicolescu
General Information
• Instructor: Dr. Monica Nicolescu
– E-mail:
[email protected]
– Office hours:
Tuesday, Thursday; 11:00am-noon
– Room:
SEM 239
• Class webpage:
– http://www.cs.unr.edu/~monica/Courses/CS493-790/
• Lectures
– Tuesday: 9:30-10:45am SEM 344
• Laboratory
– Thursday: 9:30-10:45am SEM 246
CS 493/790(X) - Lecture 1
2
What will we Learn?
• Cover fundamental aspects of robotics
– What is a robot?
– Robot control architectures
• Advanced robotics techniques
– Biologically inspired robotics
– Robot learning: reinforcement, imitation,
demonstration, genetic algorithms
– Multiple robot systems: coordination and cooperation
– Human-robot interaction
– Navigation and mapping
• Hands-on experience
CS 493/790(X) - Lecture 1
3
Readings and Presentations
• Two papers (on average) discussed at each lecture
• Each paper is presented by a student
• Presentation guidelines
– At most 30 minutes
– Briefly summarize the paper
– Discuss the paper, its strengths, weaknesses, any points
needing clarification
– Addressing any questions the other students may have
CS 493/790(X) - Lecture 1
4
Readings and Paper Reports
• For each paper, all students must submit, at the
beginning of the class a brief report of the paper
• Report format (typed)
– Student's name
– Title and authors of the paper
– A short paragraph summarizing the contributions of the
paper
– A critique of the paper that addresses the strengths and
weaknesses of the paper
CS 493/790(X) - Lecture 1
5
Project/Lab Testbeds
• The Player-Stage-Gazebo simulator
(playerstage.sourceforge.net)
– Player is a general purpose language-indepedent network
server for robot control
– Stage is a Player-compatible high-fidelity indoor multi-robot
simulation testbed
– Gazebo is a Player-compatible high-fidelity 3D outdoor
simulation testbed with dynamics
– Player/Stage/Gazebo allows for direct porting to Playercompatible physical robots.
CS 493/790(X) - Lecture 1
6
Project/Lab Testbeds
• One Player-compatible ActivMedia Pioneer 3 DX
– sonar sensors
– Laser
– PTZ camera
– Onboard computer
• One Player-compatible ActivMedia Pioneer 1 AT robot
– 7 sonar sensors and requires the use of a laptop (not provided)
• 16 LEGO robot kits
– Handy Board microcontroller
– Programming in Interactive C
CS 493/790(X) - Lecture 1
7
Project
• Individual project on topics covered in class
• Project topics: an implementation of either:
– a single robot system (involving complex behavior and
demonstrated on a physical robot) or
– a multi-robot system (involving cooperation/
communication/ coordination between robots and
demonstrated in simulation)
CS 493/790(X) - Lecture 1
8
Project Reports
• Should include the following:
– Title, author
– Abstract
– Introduction and motivation
– Problem definition: project goals, assumptions, constraints, and
evaluation criteria
– Details of proposed approach
– Results and objective experimental evaluation
– Review of relevant literature
– Discussion (strengths and weaknesses) and conclusion
– References
– Appendix (relevant code or algorithms)
CS 493/790(X) - Lecture 1
9
Class Policy
• Grading
– Paper reports: 15%
– Paper presentations: 20%
– Participation in class discussions: 15%
– Lab assignments: 20%
– Final project: 30%
• Late submissions
– No late submissions will be accepted
• Attendance
– Full participation in class discussions
CS 493/790(X) - Lecture 1
10
Important Dates/Milestones
• February 23
– Project topic proposal and presentation
– One page that outlines the specific goals, contribution,
implementation platform and the proposed approach
• April 6
– Project status presentations
– 5 minute in-class presentation
– One-two pages that describe the current status of the
project, what has been done, what is still to be done
CS 493/790(X) - Lecture 1
11
Important Dates/Milestones
• May 12
– Project final presentations
– Project final demonstrations
– Project final reports due
CS 493/790(X) - Lecture 1
12
Optional Textbooks
• Basic topics
– The Robotics Primer, 2001. Author: Maja Mataric'
– Available in draft form at the bookstore
• Advanced topics
– Behavior-Based Robotics, 2001.
Author: Ron Arkin
– Available at the library
• Lego Robots
– Robotic Explorations: An Introduction to Engineering
Through Design, 2001. Author: Fred G. Martin
CS 493/790(X) - Lecture 1
13
Key Concepts
• Situatedness
– Agents are strongly affected by the environment and deal
with its immediate demands (not its abstract models)
directly
• Embodiment
– Agents have bodies, are strongly constrained by those
bodies, and experience the world through those bodies,
which have a dynamic with the environment
CS 493/790(X) - Lecture 1
14
Key Concepts (cont.)
• Situated intelligence
– is an observed property, not necessarily internal to the
agent or to a reasoning engine; instead it results from the
dynamics of interaction of the agent and environment
– and behavior are the result of many interactions within the
system and w/ the environment, no central source or
attribution is possible
CS 493/790(X) - Lecture 1
15
The term “robot”
• Karel Capek’s 1921 play RUR (Rossum’s Universal
Robots)
– It is (most likely) a combination of “rabota” (obligatory
work) and “robotnik” (serf)
• Most real-world robots today do perform such
“obligatory work” in highly controlled environments
– Factory automation (car assembly)
• But that is not what robotics research about; the
trends and the future look much more interesting
CS 493/790(X) - Lecture 1
16
What is in a Robot?
• Sensors
• Effectors and actuators
– Used for locomotion and manipulation
• Controllers for the above systems
– Coordinating information from sensors
with commands for the robot’s actuators
• Robot = an autonomous system which exists in the
physical world, can sense its environment and can
act on it to achieve some goals
CS 493/790(X) - Lecture 1
17
Challenges
• Perception
– Limited, noisy sensors
• Actuation
– Limited capabilities of robot effectors
• Thinking
– Time consuming in large state spaces
• Environments
– Dynamic, impose fast reaction times
CS 493/790(X) - Lecture 1
18
Uncertainty
• Uncertainty is a key property of existence in the
physical world
• Physical sensors provide limited, noisy, and
inaccurate information
• Physical effectors produce limited, noisy, and
inaccurate action
• The uncertainty of physical sensors and effectors is
not well characterized, so robots have no available a
priori models
CS 493/790(X) - Lecture 1
19
Uncertainty (cont.)
• A robot cannot accurately know the answers to the
following:
– Where am I?
– Where are my body parts, are they working, what are they
doing?
– What did I just do?
– What will happen if I do X?
– Who/what are you, where are you, what are you doing,
etc.?...
CS 493/790(X) - Lecture 1
20
Classical activity decomposition
• Locomotion (moving around, going places)
– factory delivery, Mars Pathfinder, lawnmowers, vacuum
cleaners...
• Manipulation (handling objects)
– factory automation, automated surgery...
• This divides robotics into two basic areas
– mobile robotics
– manipulator robotics
• … but these are merging in domains like robot pets,
robot soccer, and humanoids
CS 493/790(X) - Lecture 1
21
Robots: Alternative Terms
• UAV
– unmanned aerial vehicle
• UGV (rover)
– unmanned ground vehicle
• UUV
– unmanned undersea vehicle
CS 493/790(X) - Lecture 1
22
An assortment of robots…
CS 493/790(X) - Lecture 1
23
Anthropomorphic Robots
CS 493/790(X) - Lecture 1
24
Animal-like Robots
CS 493/790(X) - Lecture 1
25
Humanoid Robots
QRIO
Asimo (Honda)
Robonaut (NASA)
CS 493/790(X)
- Lecture 1
DB (ATR)
Sony Dream Robot
26
A Brief History of Robotics
• Robotics grew out of the fields of control theory, cybernetics
and AI
• Robotics, in the modern sense, can be considered to have
started around the time of cybernetics (1940s)
• Early AI had a strong impact on how it evolved (1950s-1970s),
emphasizing reasoning and abstraction, removal from direct
situatedness and embodiment
• In the 1980s a new set of methods was introduced and robots
were put back into the physical world
CS 493/790(X) - Lecture 1
27
W. Grey Walter’s Tortoise
• Machina Speculatrix” (1953)
– 1 photocell, 1 bump sensor,
1 motor, 3 wheels, 1
battery
• Behaviors:
– seek light
– head toward moderate light
– back from bright light
– turn and push
– recharge battery
• Uses reactive control, with
behavior prioritization
CS 493/790(X) - Lecture 1
28
Braitenberg Vehicles
• Valentino Braitenberg (1980)
• Thought experiments
– Use direct coupling between sensors and motors
– Simple robots (“vehicles”) produce complex behaviors that
appear very animal, life-like
• Excitatory connection
– The stronger the sensory input, the stronger the motor output
– Light sensor  wheel: photophilic robot (loves the light)
• Inhibitory connection
– The stronger the sensory input, the weaker the motor output
– Light sensor  wheel: photophobic robot (afraid of the light)
CS 493/790(X) - Lecture 1
29
Example Vehicles
• Wide range of vehicles can be designed, by changing the
connections and their strength
Vehicle 1
• Vehicle 1: Being “ALIVE”
– One motor, one sensor
• Vehicle 2: “FEAR” and “AGGRESSION”
– Two motors, two sensors
Vehicle 2
– Excitatory connections
• Vehicle 3: “LOVE”
– Two motors, two sensors
– Inhibitory connections
CS 493/790(X) - Lecture 1
30
Artificial Intelligence
• Officially born in 1956 at Dartmouth University
– Marvin Minsky, John McCarthy, Herbert Simon
• Intelligence in machines
– Internal models of the world
– Search through possible solutions
– Plan to solve problems
– Symbolic representation of information
– Hierarchical system organization
– Sequential program execution
CS 493/790(X) - Lecture 1
31
AI and Robotics
• AI influence to robotics:
– Knowledge and knowledge representation are central to
intelligence
• Perception and action are more central to robotics
• New solutions developed: behavior-based systems
– “Planning is just a way of avoiding figuring out what to do
next” (Rodney Brooks, 1987)
• Distributed AI (DAI)
– Society of Mind (Marvin Minsky, 1986): simple, multiple
agents can generate highly complex intelligence
• First robots were mostly influenced by AI (deliberative)
CS 493/790(X) - Lecture 1
32
Background Readings
• F. Martin: Sections 1.1, 1.2.3
• M. Matarić: Chapters 1, 3
CS 493/790(X) - Lecture 1
33