Robotics-training-classes

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Motivation
• Intelligent Environments are aimed at improving the
inhabitants’ experience and task performance
• Automate functions in the home
• Provide services to the inhabitants
• Decisions coming from the decision maker(s) in the
environment have to be executed.
• Decisions require actions to be performed on devices
• Decisions are frequently not elementary device
interactions but rather relatively complex commands
• Decisions define set points or results that have to be achieved
• Decisions can require entire tasks to be performed
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Automation and Robotics in
Intelligent Environments
 Control of the physical environment
 Automated blinds
 Thermostats and heating ducts
 Automatic doors
 Automatic room partitioning
 Personal service robots
 House cleaning
 Lawn mowing
 Assistance to the elderly and handicapped
 Office assistants
 Security services
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Robots
• Robota (Czech) = A worker of forced labor
From Czech playwright Karel Capek's 1921 play “R.U.R”
(“Rossum's Universal Robots”)
• Japanese Industrial Robot Association (JIRA) :
“A device with degrees of freedom that can be controlled.”
• Class 1 : Manual handling device
• Class 2 : Fixed sequence robot
• Class 3 : Variable sequence robot
• Class 4 : Playback robot
• Class 5 : Numerical control robot
• Class 6 : Intelligent robot
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A Brief History of Robotics
• Mechanical Automata
• Ancient Greece & Egypt
• Water powered for ceremonies
• 14th
– 19th
century Europe
• Clockwork driven for entertainment
• Motor driven Robots
• 1928: First motor driven automata
• 1961: Unimate
• First industrial robot
• 1967: Shakey
• Autonomous mobile research robot
• 1969: Stanford Arm
• Dextrous, electric motor driven robot arm
Maillardet’s Automaton
Unimate
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Robots
 Robot Manipulators
 Mobile Robots
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Robots
 Walking Robots
 Humanoid Robots
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Autonomous Robots
• The control of autonomous robots involves a
number of subtasks
• Understanding and modeling of the mechanism
• Kinematics, Dynamics, and Odometry
• Reliable control of the actuators
• Closed-loop control
• Generation of task-specific motions
• Path planning
• Integration of sensors
• Selection and interfacing of various types of sensors
• Coping with noise and uncertainty
• Filtering of sensor noise and actuator uncertainty
• Creation of flexible control policies
• Control has to deal with new situations
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Traditional Industrial Robots
• Traditional industrial robot control uses robot arms
and largely pre-computed motions
 Programming using “teach box”
 Repetitive tasks
 High speed
 Few sensing operations
 High precision movements
 Pre-planned trajectories and
task policies
 No interaction with humans
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Problems
• Traditional programming techniques for industrial
robots lack key capabilities necessary in intelligent
environments
 Only limited on-line sensing
 No incorporation of uncertainty
 No interaction with humans
 Reliance on perfect task information
 Complete re-programming for new tasks
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Requirements for Robots in
• Autonomy
• Robots have to be capable of achieving task objectives
without human input
• Robots have to be able to make and execute their own
decisions based on sensor information
• Intuitive Human-Robot Interfaces
• Use of robots in smart homes can not require extensive
user training
• Commands to robots should be natural for inhabitants
• Adaptation
• Robots have to be able to adjust to changes in the
environment
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Robots for Intelligent
Environments
• Service Robots
• Security guard
• Delivery
• Cleaning
• Mowing
• Assistance Robots
• Mobility
• Services for elderly and
People with disabilities
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Autonomous Robot Control
• To control robots to perform tasks autonomously a
number of tasks have to be addressed:
• Modeling of robot mechanisms
• Kinematics, Dynamics
• Robot sensor selection
• Active and passive proximity sensors
• Low-level control of actuators
• Closed-loop control
• Control architectures
• Traditional planning architectures
• Behavior-based control architectures
• Hybrid architectures
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Modeling the Robot
Mechanism
• Forward kinematics describes how the robots joint
angle configurations translate to locations in the
world
• Inverse kinematics computes the joint angle
configuration necessary to reach a particular point
in space.
• Jacobians calculate how the speed and
configuration of the actuators translate into velocity
of the robot
(x, y, z)
θ1
θ2
(x, y, θ)
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Mobile Robot Odometry
• In mobile robots the same configuration in terms of
joint angles does not identify a unique location
• To keep track of the robot it is necessary to incrementally
update the location (this process is called odometry or
dead reckoning)
• Example: A differential drive robot
(x, y, θ)
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Actuator Control
• To get a particular robot actuator to a particular
location it is important to apply the correct amount
of force or torque to it.
• Requires knowledge of the dynamics of the robot
• Mass, inertia, friction
• For a simplistic mobile robot: F = m a + B v
• Frequently actuators are treated as if they were
independent (i.e. as if moving one joint would not affect
any of the other joints).
• The most common control approach is PD-control
(proportional, differential control)
• For the simplistic mobile robot moving in the x direction:
( ) ( )actualdesiredDactualdesiredP vvKxxKF −+−=
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Robot Navigation
• Path planning addresses the task of computing a
trajectory for the robot such that it reaches the
desired goal without colliding with obstacles
• Optimal paths are hard to compute in particular for robots
that can not move in arbitrary directions (i.e.
nonholonomic robots)
• Shortest distance paths can be dangerous since they
always graze obstacles
• Paths for robot arms have to take into account the entire
robot (not only the endeffector)
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Sensor-Driven Robot Control
• To accurately achieve a task in an intelligent
environment, a robot has to be able to react
dynamically to changes ion its surrounding
• Robots need sensors to perceive the environment
• Most robots use a set of different sensors
• Different sensors serve different purposes
• Information from sensors has to be integrated into the
control of the robot
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Robot Sensors
• Internal sensors to measure the robot configuration
• Encoders measure the rotation angle of a joint
• Limit switches detect when the joint has reached the limit
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Robot Sensors
• Proximity sensors are used to measure the distance or
location of objects in the environment. This can then be used
to determine the location of the robot.
• Infrared sensors determine the distance to an object by measuring the
amount of infrared light the object reflects back to the robot
• Ultrasonic sensors (sonars) measure the time that an ultrasonic signal
takes until it returns to the robot
• Laser range finders determine distance by
measuring either the time it takes for a laser
beam to be reflected back to the robot or by
measuring where the laser hits the object
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Robot Sensors
• Computer Vision provides robots with the capability
to passively observe the environment
• Stereo vision systems provide complete location
information using triangulation
• However, computer vision is very complex
• Correspondence problem makes stereo vision even more difficult
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Uncertainty in Robot Systems
Robot systems in intelligent environments have to
deal with sensor noise and uncertainty
 Sensor uncertainty
Sensor readings are imprecise and unreliable
 Non-observability
Various aspects of the environment can not be observed
The environment is initially unknown
 Action uncertainty
Actions can fail
Actions have nondeterministic outcomes
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Probabilistic Robot Localization
Explicit reasoning about
Uncertainty using Bayes
filters:
Used for:
 Localization
 Mapping
 Model building
1111 )(),|()|()( −−−−∫= tttttttt dxxbaxxpxopxb η
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Deliberative
Robot Control Architectures
• In a deliberative control architecture the robot first
plans a solution for the task by reasoning about the
outcome of its actions and then executes it
• Control process goes through a sequence of sencing,
model update, and planning steps
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Deliberative
Control Architectures
• Advantages
• Reasons about contingencies
• Computes solutions to the given task
• Goal-directed strategies
• Problems
• Solutions tend to be fragile in the presence of uncertainty
• Requires frequent replanning
• Reacts relatively slowly to changes and unexpected
occurrences
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Behavior-Based
• In a behavior-based control architecture the robot’s
actions are determined by a set of parallel, reactive
behaviors which map sensory input and state to
actions.
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Behavior-Based
• Reactive, behavior-based control combines
relatively simple behaviors, each of which achieves a
particular subtask, to achieve the overall task.
• Robot can react fast to changes
• System does not depend on complete knowledge of the
environment
• Emergent behavior (resulting from combining initial
behaviors) can make it difficult to predict exact behavior
• Difficult to assure that the overall task is achieved
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Complex Behavior from Simple
Elements: Braitenberg Vehicles
• Complex behavior can be achieved using very simple
control mechanisms
• Braitenberg vehicles: differential drive mobile robots with
two light sensors
• Complex external behavior does not necessarily require a complex
+ +
“Coward” “Aggressive”
+ + - -
“Love” “Explore”
- -
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Behavior-Based
Architectures: Subsumption
Example
• Subsumption architecture is one of the earliest
behavior-based architectures
• Behaviors are arranged in a strict priority order where
higher priority behaviors subsume lower priority ones as
long as they are not inhibited.
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Subsumption Example
• A variety of tasks can be robustly performed from a
small number of behavioral elements
© MIT AI Lab
http://www-robotics.usc.edu/~maja/robot-video.mpg
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Reactive, Behavior-Based
Control Architectures
• Advantages
• Reacts fast to changes
• Does not rely on accurate models
• “The world is its own best model”
• No need for replanning
• Problems
• Difficult to anticipate what effect combinations of
behaviors will have
• Difficult to construct strategies that will achieve complex,
novel tasks
• Requires redesign of control system for new tasks
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Hybrid Control Architectures
 Hybrid architectures combine
reactive control with abstract
task planning
 Abstract task planning layer
 Deliberative decisions
 Plans goal directed policies
 Reactive behavior layer
 Provides reactive actions
 Handles sensors and actuators
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Hybrid Control Policies
Task Plan:
Behavioral
Strategy:
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Example Task:
Changing a Light Bulb
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Hybrid Control Architectures
• Advantages
• Permits goal-based strategies
• Ensures fast reactions to unexpected changes
• Reduces complexity of planning
• Problems
• Choice of behaviors limits range of possible tasks
• Behavior interactions have to be well modeled to be able
to form plans
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Traditional Human-Robot
Interface: Teleoperation
Remote Teleoperation: Direct
operation of the robot by the
user
 User uses a 3-D joystick or an
exoskeleton to drive the robot
 Simple to install
 Removes user from dangerous areas
 Problems:
 Requires insight into the mechanism
 Can be exhaustive
 Easily leads to operation errors
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Human-Robot Interaction in
• Personal service robot
• Controlled and used by untrained users
• Intuitive, easy to use interface
• Interface has to “filter” user input
• Eliminate dangerous instructions
• Find closest possible action
• Receive only intermittent commands
• Robot requires autonomous capabilities
• User commands can be at various levels of complexity
• Control system merges instructions and autonomous operation
• Interact with a variety of humans
• Humans have to feel “comfortable” around robots
• Robots have to communicate intentions in a natural way
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Example: Minerva the Tour
Guide Robot (CMU/Bonn)
© CMU Robotics Institute
http://www.cs.cmu.edu/~thrun/movies/minerva.mpg
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Intuitive Robot Interfaces:
Command Input
• Graphical programming interfaces
• Users construct policies form elemental blocks
• Problems:
• Requires substantial understanding of the robot
• Deictic (pointing) interfaces
• Humans point at desired targets in the world or
• Target specification on a computer screen
• Problems:
• How to interpret human gestures ?
• Voice recognition
• Humans instruct the robot verbally
• Problems:
• Speech recognition is very difficult
• Robot actions corresponding to words has to be defined
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Intuitive Robot Interfaces:
Robot-Human Interaction
• He robot has to be able to communicate its
intentions to the human
• Output has to be easy to understand by humans
• Robot has to be able to encode its intention
• Interface has to keep human’s attention without annoying
her
• Robot communication devices:
• Easy to understand computer screens
• Speech synthesis
• Robot “gestures”
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Human-Robot Interfaces
• Existing technologies
• Simple voice recognition and speech synthesis
• Gesture recognition systems
• On-screen, text-based interaction
• Research challenges
• How to convey robot intentions ?
• How to infer user intent from visual observation (how can
a robot imitate a human) ?
• How to keep the attention of a human on the robot ?
• How to integrate human input with autonomous operation
?
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Integration of Commands and
Autonomous Operation
 Adjustable Autonomy
 The robot can operate at
varying levels of autonomy
 Operational modes:

Autonomous operation

User operation / teleoperation

Behavioral programming

Following user instructions

Imitation
 Types of user commands:

Continuous, low-level
instructions (teleoperation)

Goal specifications

Task demonstrations Example System
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"Social" Robot Interactions
 To make robots acceptable to average users
they should appear and behave “natural”
 "Attentional" Robots
 Robot focuses on the user or the task
 Attention forms the first step to imitation
 "Emotional" Robots
 Robot exhibits “emotional” responses
 Robot follows human social norms for behavior
 Better acceptance by the user (users are more forgiving)
 Human-machine interaction appears more “natural”
 Robot can influence how the human reacts
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"Social" Robot Interactions
 Advantages:
 Robots that look human and that show “emotions”
can make interactions more “natural”
 Humans tend to focus more attention on people than on
objects
 Humans tend to be more forgiving when a mistake is
made if it looks “human”
 Robots showing “emotions” can modify the way in
which humans interact with them
 Problems:
 How can robots determine the right emotion ?
 How can “emotions” be expressed by a robot ?
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Human-Robot Interfaces for
• Robot Interfaces have to be easy to use
• Robots have to be controllable by untrained users
• Robots have to be able to interact not only with their owner
but also with other people
• Robot interfaces have to be usable at the human’s
discretion
• Human-robot interaction occurs on an irregular basis
• Frequently the robot has to operate autonomously
• Whenever user input is provided the robot has to react to it
• Interfaces have to be designed human-centric
• The role of the robot is it to make the human’s life easier
and more comfortable (it is not just a tech toy)
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 Intelligent Environments are non-stationary and
change frequently, requiring robots to adapt
 Adaptation to changes in the environment
 Learning to address changes in inhabitant preferences
 Robots in intelligent environments can frequently
not be pre-programmed
 The environment is unknown
 The list of tasks that the robot should perform might
not be known beforehand
 No proliferation of robots in the home
 Different users have different preferences
Adaptation and Learning for
Robots in Smart Homes
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Adaptation and Learning
In Autonomous Robots
 Learning to interpret sensor information
 Recognizing objects in the environment is difficult
 Sensors provide prohibitively large amounts of data
 Programming of all required objects is generally not
possible
 Learning new strategies and tasks
 New tasks have to be learned on-line in the home
 Different inhabitants require new strategies even for
existing tasks
 Adaptation of existing control policies
 User preferences can change dynamically
 Changes in the environment have to be reflected
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Learning Approaches for
Robot Systems
 Supervised learning by teaching
 Robots can learn from direct feedback from the
user that indicates the correct strategy
 The robot learns the exact strategy provided by the user
 Learning from demonstration (Imitation)
 Robots learn by observing a human or a robot
perform the required task
 The robot has to be able to “understand” what it observes
and map it onto its own capabilities
 Learning by exploration
 Robots can learn autonomously by trying different
actions and observing their results
 The robot learns a strategy that optimizes reward
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Learning Sensory Patterns
Chair
 Learning to Identify Objects
 How can a particular object be
recognized ?

Programming recognition strategies is
difficult because we do not fully
understand how we perform recognition
 Learning techniques permit the robot
system to form its own recognition
strategy
 Supervised learning can be used by
giving the robot a set of pictures and
the corresponding classification
 Neural networks
 Decision trees
:
:
:
:
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Learning Task Strategies by
Experimentation
 Autonomous robots have to be able to learn
new tasks even without input from the user
 Learning to perform a task in order to optimize the
reward the robot obtains (Reinforcement Learning)
 Reward has to be provided either by the user or the
environment
 Intermittent user feedback
 Generic rewards indicating unsafe or inconvenient actions or
occurrences
 The robot has to explore its actions to determine what
their effects are
 Actions change the state of the environment
 Actions achieve different amounts of reward
 During learning the robot has to maintain a level of safety
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Example: Reinforcement
Learning in a Hybrid Architecture
 Policy Acquisition Layer
 Learning tasks without
supervision
 Abstract Plan Layer
 Learning a system model
 Basic state space
compression
 Reactive Behavior Layer
 Initial competence and
reactivity
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Example Task:
Learning to Walk
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Scaling Up: Learning
Complex Tasks from Simpler
Tasks
 Complex tasks are hard to learn since they
involve long sequences of actions that have to
be correct in order for reward to be obtained
 Complex tasks can be learned as shorter
sequences of simpler tasks
 Control strategies that are expressed in terms of
subgoals are more compact and simpler
 Fewer conditions have to be considered if simpler
tasks are already solved
 New tasks can be learned faster
 Hierarchical Reinforcement Learning
 Learning with abstract actions
 Acquisition of abstract task knowledge
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Example: Learning to Walk
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