Robotics 1

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Presentation Transcript

Smart Home Technologies: 

Smart Home Technologies Automation and Robotics


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

Automation and Robotics in Intelligent Environments: 

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


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

A Brief History of Robotics: 

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


Robots Robot Manipulators Mobile Robots


Robots Walking Robots Humanoid Robots

Autonomous Robots: 

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

Traditional Industrial Robots: 

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

Problems : 

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

Requirements for Robots in Intelligent Environments: 

Requirements for Robots in Intelligent Environments 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

Robots for Intelligent Environments: 

Robots for Intelligent Environments Service Robots Security guard Delivery Cleaning Mowing Assistance Robots Mobility Services for elderly and People with disabilities

Autonomous Robot Control: 

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

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 Modeling the Robot Mechanism

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 Mobile Robot Odometry R L

Actuator Control: 

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:

Robot Navigation: 

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)

Sensor-Driven Robot Control: 

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

Robot Sensors: 

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

Robot Sensors: 

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

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 Robot Sensors


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


Probabilistic Robot Localization Explicit reasoning about Uncertainty using Bayes filters: Used for: Localization Mapping Model building

Deliberative Robot Control Architectures: 

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

Deliberative Control Architectures: 

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

Behavior-Based Robot Control Architectures: 

Behavior-Based Robot Control Architectures 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.

Behavior-Based Robot Control Architectures: 

Behavior-Based Robot Control Architectures 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

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 reasoning mechanism Complex Behavior from Simple Elements: Braitenberg Vehicles “Coward” “Aggressive” “Love” “Explore”

Behavior-Based Architectures: Subsumption Example: 

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.

Subsumption Example: 

Subsumption Example A variety of tasks can be robustly performed from a small number of behavioral elements © MIT AI Lab

Reactive, Behavior-Based Control Architectures: 

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


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


Hybrid Control Policies Task Plan: Behavioral Strategy:


Example Task: Changing a Light Bulb

Hybrid Control Architectures: 

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


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

Human-Robot Interaction in Intelligent Environments: 

Human-Robot Interaction in Intelligent Environments 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


Example: Minerva the Tour Guide Robot (CMU/Bonn) © CMU Robotics Institute

Intuitive Robot Interfaces: Command Input: 

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

Intuitive Robot Interfaces: Robot-Human Interaction: 

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”


Example: The Nursebot Project © CMU Robotics Institute http://www/cs/

Human-Robot Interfaces: 

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 ?


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


"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


"Social" Robot Example: Kismet © MIT AI Lab


"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 ?

Human-Robot Interfaces for Intelligent Environments: 

Human-Robot Interfaces for Intelligent Environments 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)


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


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


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


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


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


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


Example Task: Learning to Walk


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


Example: Learning to Walk


Conclusions Robots are an important component in Intelligent Environments Automate devices Provide physical services Robot Systems in these environments need particular capabilities Autonomous control systems Simple and natural human-robot interface Adaptive and learning capabilities Robots have to maintain safety during operation While a number of techniques to address these requirements exist, no functional, satisfactory solutions have yet been developed Only very simple robots for single tasks in intelligent environments exist

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