CSC 8520 Spring 2010. Paula MatuszekSlides based in part on and -part2.ppt CS 8520: Artificial

CSC 8520 Spring 2010. Paula Matuszek Slides based in part on www.jhu.edu/virtlab/course-info/ei/ppt/robotics-part1.ppt and -part2.ppt

CS 8520: Artificial Intelligence

Robotics

Paula Matuszek

Spring, 2010

2CSC 8520 Spring 2010. Paula Matuszek Slides based in part on www.jhu.edu/virtlab/course-info/ei/ppt/robotics-part1.ppt and -part2.ppt 2

What is your favorite robot?

HAL 9000. 2001, A Space Odyssey: 1968

Wall-e: 2008

Data. Star Trek: TNG: 1987

Robby. Forbidden Planet: 1956

Cylons and Centurion. BSG:2009.

3CSC 8520 Spring 2010. Paula Matuszek Slides based in part on www.jhu.edu/virtlab/course-info/ei/ppt/robotics-part1.ppt and -part2.ppt

Some 21st century robots



“A robot is a reprogrammable, multifunctional manipulator designed to move material, parts, tools, or specialized devices through variable programmed motions for the performance of a variety of tasks.” (Robot Institute of America)

Definition:

Alternate definition:

“A robot is a one-armed, blind idiot with limited memory and which cannot speak, see, or hear.”

What is a robot?


What are robots good at?• What is hard for humans is easy for robots.

– Repetitive tasks.

– Continuous operation.

– Complicated calculations.

– Refer to huge databases.

• What is easy for a human is hard for robots. – Reasoning.

– Adapting to new situations.

– Flexible to changing requirements.

– Integrating multiple sensors.

– Resolving conflicting data.

– Synthesizing unrelated information.

– Creativity.


What tasks would you give robots?

• Dangerous– space exploration

– chemical spill cleanup

– disarming bombs

– disaster cleanup

• Boring and/or repetitive– welding car frames

– part pick and place

– manufacturing parts.

• High precision or high speed– electronics testing

– surgery

– precision machining.


Categories of Robots• Manipulators

– Anchored somewhere: factory assembly lines, International Space Station, hospitals.

– Common industrial robots

• Mobile Robots– Move around environment

– UGVs, UAVs, AUVs, UUVs

– Mars rovers, delivery bots, ocean explorers

• Mobile Manipulators– Both move and manipulate

– Packbot, humanoid robots


What subsystems make up a robot?

• Sensors– Stationary base– Mobile

• Actuators

• Control/Software

Robert Stengel, Princeton Univ.

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Sensors• Perceive the world

– Passive sensors capture signals generated by environment. Background, lower power. E.G.: cameras.

– Active sensors probe the environment. Explicitly triggered, more info, higher power consumption. E.G. sonar

• What are they sensing– The environment: e.g. range finders, obstacle detection

– The robot’s location: e.g., gps, wireless stations

– Robot’s own internals: proprioceptive sensors. e.g.: shaft decoders

• Stop and think about that one for a moment. Close your eyes - where’s your hand? Move it - where is it now?

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What use are sensors?

• Uses sensors for feedback – Closed-loop robots use sensors in

conjunction with actuators to gain higher accuracy – servo motors.

– Uses include mobile robotics, telepresence, search and rescue, pick and place with machine vision, anything involving human interaction

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Some typical sensors

• Optical– Laser / radar

– 3D

– Color spectrum

• Pressure

• Temperature

• Chemical

• Motion & Accelerometer

• Acoustic– Ultrasonic

• E-field Sensing

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Effectors• Take some kind of action in the world

• Involve movement of robot or subcomponent of robot

• Robot actions could include

– Pick and place: Move items between points

– Continuous path control: Move along a programmable path

– Sensory: Employ sensors for feedback (e-field sensing)

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Some kinds of Actuators• Actuators

– pneumatic

– hydraulic

– electric solenoid

• Motors– Analog (continuous)

– Stepping (discrete increments)

• Gears, belts, screws, levers

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Mobility• Legs

• Wheels

• Tracks

• Crawls

• Rolls

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• Simple joints (2D)– Translation/Prismatic — sliding

along one axis• square cylinder in square tube

– Rotation.Revolute — rotating about one axis

• Compound joints (3D)– ball and socket = 3 revolute joints

– round cylinder in tube = 1 prismatic, 1 revolute

How do robots move?

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Degrees of Freedom (DOF)• Degrees of freedom = Number of independent directions a

robot or its manipulator can move– 3 degrees of freedom: 2 translation, 1 rotation

– 6 degrees of freedom: 3 translation, 3 rotation

• How many degrees of freedom does your knee have? Your elbow?

• Effective DOF vs controllable DOF:

– Underwater explorer might have up or down, left or right, rolling. 3 controllable DOF.

– Position includes x,y,z coordinates, yaw, roll, pitch. (together the pose or kinematic state). 6 effective DOF.

• Holonomic: effective DOF = controllable DOF.

http://www.nasm.si.edu/exhibitions/gal109/newhtf/htf541b.htm

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Control - the Brain

• Open loop, i.e., no feedback, deterministic – Instructions

– Rules

• Closed loop, i.e., feedback– Learn

– Adapt

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What are some problems with control of robot actions?

• Joint play, compounded through N joints.

• Accelerating masses produce vibration, elastic deformations in links.

• Torques, stresses transmitted depending on end actuator loads.

• Feedback loop creates instabilities.– Delay between sensing and reaction.

• Firmware and software problems– Especially with more intelligent approaches

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Robotic Perception• Sensing isn’t enough: need to act on data sensed

• Hard because data are noisy; environment is dynamic and partially observable.

• Must be mapped into an internal representation– state estimation

• Good representations– contain enough information for good decisions

– structured for efficient updating

– natural mapping between representation and real world.

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Belief State• Belief state: model of the state of the

environment (including the robot)– X: set of variables describing the environment

– Xt: state at time t

– Zt: observation received at time t

– At: action taken after Zt is observed

• After At, compute new belief state Xt+1

• Probabilistic, because uncertainty in both Xt and Zt.

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Some Perception Problems• Localization: where is the robot, where are other

things in the environment– landmarks

– range scans

• Mapping: no map given, robot must determine both environment and position. – SLAM: Simultaneous localization and mapping

• Probabilistic approaches typical, but cumbersome

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Software Architectures • Low-level, reactive control

– bottom-up, sensor results directly trigger actions

• Model-based, deliberative planning– top-down, actions are triggered based on

planning around a state model

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Low-Level, Reactive Control• Augmented finite state machines

• Sensed inputs and a clock determine next state

• Build bottom up, from individual motions

• Subsumption architecture synchronizes AFSMs, combines values from separate AFSMs.

• Advantages: simple to develop, fast

• Disadvantages: Fragile for bad sensor data, don’t support integration of complex data over time.

• Typically used for simple tasks, like following a wall or moving a leg.

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Model-Based Deliberative Planning• Belief State model

– Current State, Goal State

– Any of planning techniques

– Typically use probabilistic methods

• Advantages: can handle uncertain measurements and complex integrations, can be responsive to change or problems.

• Disadvantages: slow; current algorithms can take minutes. Developing models for the number of actions involved in driving a complex robot too cumbersome.

• Typically used for high-level actions such as whether to move and in which direction.

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Hybrid Architectures• Usually, actually doing anything requires both

reactive and deliberative processing.• Typical architecture is three-layer:

– Reactive Layer: low-level control, tight senso-action loop, decision cycle of milliseconds

– Deliberative layer: global solutions to complex tasks, model-based planning, decision cycle of minutes

– Executive layer: glue. Accepts directions from deliberative layer, sequences actions for reactive layer, decision cycle of a second

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How do you measures of performance of robot?

• Speed and acceleration• Resolution• Working volume• Accuracy• Cost• Plus all the kinds of evaluation functions we have

talked about for any AI system.

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Measures of Performance• Speed and acceleration

– Faster speed often reduces resolution or increases cost– Varies depending on position, load.– Speed can be limited by the task the robot performs (welding,

cutting)

• Resolution– Often a speed tradeoff

– The smallest movement the robot can make• Working volume

– The space within which the robot operates.– Larger volume costs more but can increase the capabilities of a

robot

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Where are robots working now?• Healthcare and personal care

– surgical aids, intelligent walkers, eldercare

• Personal services– Roomba! Information kiosks, lawn mowers, golf

caddies, museum guides

• Entertainment– sports (robotic soccer)

• Human augmentation– walking machines, exoskeletons, robotic hands, etc.

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And more...• Industry and Agriculture

– assembly, welding, painting, harvesting, mining, pick-and-place, packaging, inspection, ...

• Transportation– Autonomous helicopters, pilot

assistance, materials movement

• Cars (DARPA Grand Challenge, Urban Challenge)– Antilock brakes, lane

following, collision detection

• Exploration and Hazardous environments

– Mars rovers, search and rescue, underwater and mine exploration, mine detection

• Military– Reconnaissance, sentry, S&R,

combat, EOD

• Household– Cleaning, mopping, ironing,

tending bar, entertainment, telepresence/surveillance

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Tomorrow’s problems• Mechanisms

– Morphology: What should robots look like?

– Novel actuators/sensors

• Estimation and Learning– Reinforcement Learning

– Graphical Models

– Learning by Demonstration

• Manipulation (grasping)– What does the far side of an object look like? How

heavy is it? How hard should it be gripped? How can it rotate? Regrasping?

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And more...• Medical robotics

– Autonomous surgery

– Eldercare

• Biological Robots– Biomimetic robots

– Neurobotics

• Navigation– Collision avoidance

– SLAM/Exploration

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Self-X Robots• Self-feeding– Literally– Electrically

• Self-replicating• Self-repairing• Self-assembly• Self-organization• Self-reconfiguration

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Human-Robot Interaction• Social robots

– In care contexts– In home contexts– In industrial contexts

• Comprehension– Natural language– Grounded knowledge acquisition– Roomba: “Uh-oh”

For example...For example...

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Human-Robot Interaction• Social robots

• Safety/security– Ubiquitous Robotics– Small, special-purpose robots

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More Human-Robot Interaction• How do humans handle it?

– Assumptions about retention and understanding– Anthropomorphization

• How do robots make it easier?– Apologize vs. back off– Convey intent– Cultural context (implicit

vs. explicit communication)

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The Future of Robotics.

Robots that can learn. Robots with artificial intelligence. Robots that make other robots. Swarms

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Some good robotics videos.

• Swimming fish: – http://vger.aa.washington.edu/research.html

– http://www.newscientist.com/article/dn14101-shoal-of-robot-fish-casts-a-wider-data-net.html

• Robot wars: – http://robogames.net/videos.php

• Japanese robots:– http://www.ecst.csuchico.edu/~renner/Teaching/Robotics/videos.html (note: about half the

links are broken)

• Social robots: – http://www.ai.mit.edu/projects/humanoid-robotics-group/kismet/kismet.html

• Miscellaneous Robots:– http://www.newscientist.com/article/dn9972-video-top-10-robots.html

– http://grinding.be/category/robots

• Swarms– http://www.youtube.com/watch?v=SkvpEfAPXn4

http://vger.aa.washington.edu/research.html

http://robogames.net/videos.php

http://www.ecst.csuchico.edu/~renner/Teaching/Robotics/videos.html

http://www.ai.mit.edu/projects/humanoid-robotics-group/kismet/kismet.html

http://www.newscientist.com/article/dn9972-video-top-10-robots.html

http://grinding.be/category/robots

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Will robots take over the world?• Which decisions can the machine

make without human supervision?• May machine-intelligent systems

make mistakes (at the same level as humans)?

• May intelligent systems gamble when uncertain (as humans do)?

• Can (or should) intelligent systems exhibit personality?

• Can (or should) intelligent systems express emotion?

• How much information should the machine display to the human operator?

HAL - 2001 Space Odyssey

Documents

CSC 8520 Spring 2010. Paula MatuszekSlides based in part on and -part2.ppt CS 8520: Artificial