Inventing Hybrid Control The basic idea is simple: we want the best of both worlds (if possible). The goal is to combine closed-loop and open-loop execution.

Slides:



Advertisements
Similar presentations
Campus02.at don't stop thinking about tomorrow DI Anton Scheibelmasser Setubal ICINCO /25 Device integration into automation systems with.
Advertisements

ARCHITECTURES FOR ARTIFICIAL INTELLIGENCE SYSTEMS
Lecture 8: Three-Level Architectures CS 344R: Robotics Benjamin Kuipers.
AuRA: Principles and Practice in Review
1 Introduction to Robotics 13. Deliberative and Hybrid Control.
7 Introduction to AI Robotics (MIT Press), copyright Robin Murphy 2000 Chapter 7: Hybrid Deliberative/Reactive Paradigm1 Part 1: Overview & Managerial.
Lecture 6: Hybrid Robot Control Gal A. Kaminka Introduction to Robots and Multi-Robot Systems Agents in Physical and Virtual Environments.
Autonomous Mobile Robots CPE 470/670 Lecture 12 Instructor: Monica Nicolescu.
Autonomous Mobile Robots CPE 470/670 Lecture 11 Instructor: Monica Nicolescu.
ECE 4340/7340 Exam #2 Review Winter Sensing and Perception CMUcam and image representation (RGB, YUV) Percept; logical sensors Logical redundancy.
Topics: Introduction to Robotics CS 491/691(X) Lecture 11 Instructor: Monica Nicolescu.
Autonomous Mobile Robots CPE 470/670 Lecture 11 Instructor: Monica Nicolescu.
Experiences with an Architecture for Intelligent Reactive Agents By R. Peter Bonasso, R. James Firby, Erann Gat, David Kortenkamp, David P Miller, Marc.
Autonomous Mobile Robots CPE 470/670 Lecture 12 Instructor: Monica Nicolescu.
Motor Schema Based Navigation for a Mobile Robot: An Approach to Programming by Behavior Ronald C. Arkin Reviewed By: Chris Miles.
Behavior Based Robotic دکتر سعید شیری قیداری  کتاب Behavior Based Robotic Ronald C. Arkin Amirkabir University of Technology Computer Engineering & Information.
BDI Agents Martin Beer, School of Computing & Management Sciences,
Topics: Introduction to Robotics CS 491/691(X) Lecture 12 Instructor: Monica Nicolescu.
AuRA: Autonomous Robot Architecture From: Integrating Behavioral, Perceptual, and World Knowledge in Reactive Navigation Ron Arkin, 1990.
What is it? A mobile robotics system controls a manned or partially manned vehicle-car, submarine, space vehicle | Website for Students.
Unit 3a Industrial Control Systems
Mobile Robot Control Architectures “A Robust Layered Control System for a Mobile Robot” -- Brooks 1986 “On Three-Layer Architectures” -- Gat 1998? Presented.
Introduction to Behavior- Based Robotics Based on the book Behavior- Based Robotics by Ronald C. Arkin.
Chapter 2 The process Process, Methods, and Tools
Flakey Flakey's BackFlakey's Front. Flakey's Control Architecture The following is cited from the SRI web pages: Overview SRI's mobile robot, Flakey,
Robotica Lecture 3. 2 Robot Control Robot control is the mean by which the sensing and action of a robot are coordinated The infinitely many possible.
Autonomous Mobile Robots CPE 470/670 Lecture 11 Instructor: Monica Nicolescu.
European Network of Excellence in AI Planning Intelligent Planning & Scheduling An Innovative Software Technology Susanne Biundo.
ROBOTICS COE 584 Autonomous Mobile Robots. Review Definitions –Robots, robotics Robot components –Sensors, actuators, control State, state space Representation.
7 Introduction to AI Robotics (MIT Press), copyright Robin Murphy 2000 Chapter 7: Hybrid Deliberative/Reactive Paradigm1 Part 1: Overview & Managerial.
Outline: Biological Metaphor Biological generalization How AI applied this Ramifications for HRI How the resulting AI architecture relates to automation.
Introduction to Robotics In the name of Allah. Introduction to Robotics o Leila Sharif o o Lecture #3: A Brief.
7 Introduction to AI Robotics (MIT Press), copyright Robin Murphy 2000 Chapter 7: Hybrid Deliberative/Reactive Paradigm1 Part 1: Overview & Managerial.
Robotica Lecture 3. 2 Robot Control Robot control is the mean by which the sensing and action of a robot are coordinated The infinitely many possible.
Robotics Sharif In the name of Allah. Robotics Sharif Introduction to Robotics o Leila Sharif o o Lecture #3: The.
University of Amsterdam Search, Navigate, and Actuate - Qualitative Navigation Arnoud Visser 1 Search, Navigate, and Actuate Qualitative Navigation.
Marcelo R.N. Mendes. What is FINCoS? A Java-based set of tools for data generation, load submission, and performance measurement of event processing systems;
Architecture for Autonomous Assembly 1 Reid Simmons Robotics Institute Carnegie Mellon University.
University of Windsor School of Computer Science Topics in Artificial Intelligence Fall 2008 Sept 11, 2008.
Structured Control for Autonomous Robots Reid G. Simmons Carnegie Mellon University Uday Rajanna.
Intelligent Robotics Today: Robot Control Architectures Next Week: Localization Reading: Murphy Sections 2.1, 2.3, 2.5, 3.1, 3.5, 3.6, 4.1 – 4.3, 4.5,
Georgia Tech / Mobile Intelligence 1 Multi-Level Learning in Hybrid Deliberative/Reactive Mobile Robot Architectural Software Systems DARPA MARS Kickoff.
Robotica Lecture Review Reactive control Complete control space Action selection The subsumption architecture –Vertical vs. horizontal decomposition.
Introduction to Artificial Intelligence CS 438 Spring 2008 Today –AIMA, Ch. 25 –Robotics Thursday –Robotics continued Home Work due next Tuesday –Ch. 13:
Mike Graves Summer 2005 University of Texas at Dallas Implicit Invocation: The Task Control Architecture Mike Graves CS6362 Term Paper Dr. Lawrence Chung.
Selection of Behavioral Parameters: Integration of Case-Based Reasoning with Learning Momentum Brian Lee, Maxim Likhachev, and Ronald C. Arkin Mobile Robot.
Autonomous Mobile Robots CPE 470/670 Lecture 10 Instructor: Monica Nicolescu.
Trends in Robotics Research Classical AI Robotics (mid-70’s) Sense-Plan-Act Complex world model and reasoning Reactive Paradigm (mid-80’s) No models: “the.
Learning Behavioral Parameterization Using Spatio-Temporal Case-Based Reasoning Maxim Likhachev, Michael Kaess, and Ronald C. Arkin Mobile Robot Laboratory.
OBJECT-ORIENTED TESTING. TESTING OOA AND OOD MODELS Analysis and design models cannot be tested in the conventional sense. However, formal technical reviews.
1 Creating Situational Awareness with Data Trending and Monitoring Zhenping Li, J.P. Douglas, and Ken. Mitchell Arctic Slope Technical Services.
Functionality of objects through observation and Interaction Ruzena Bajcsy based on Luca Bogoni’s Ph.D thesis April 2016.
Robot Control. Open Loop Control Sends commands to make a robot preform some movement without attempting to check if it is doing things properly. For.
Matt Loper / Brown University Presented for CS296-3 February 14th, 2007 On Three Layer Architectures (Erann Gat) On Three Layer Architectures (Erann Gat)
Robotics From the book :
Learning Fast and Slow John E. Laird
Deliberative control for satellite-guided water quality monitoring
Automation as the Subject of Mechanical Engineer’s interest
CS b659: Intelligent Robotics
IEEE Std 1074: Standard for Software Lifecycle
Today: Classic & AI Control Wednesday: Image Processing/Vision
Software Design Methodology
Trends in Robotics Research
Web-Mining Agents Cooperating Agents for Information Retrieval
Deliberative & Hybrid Control
Chapter 7: Hybrid Deliberative/Reactive Paradigm
Utility-Function based Resource Allocation for Adaptable Applications in Dynamic, Distributed Real-Time Systems Presenter: David Fleeman {
Self-Managed Systems: an Architectural Challenge
Behavior Based Systems
Presentation transcript:

Inventing Hybrid Control The basic idea is simple: we want the best of both worlds (if possible). The goal is to combine closed-loop and open-loop execution. That means to combine reactive and deliberative control. This implies combining the different time-scales and representations. This mix is called hybrid control. Hybrid robotic architectures believe that a union of deliberative and behavior-based approaches can potentially yield the best of both worlds.

Strategic Global Planning Tactical Intermediate Planning Short-Term Local Planning Actuator Control Actions Global Knowledge Local World Model Intermediate Sensor Interpretations Sensing Real - Time Time Horizon Long - Term Spatial Scope Global Immediate Vicinity Hierarchical Planner World Model 4

Organizing Hybrid Systems Planning and reaction can be tied: A: hierarchical integration - planning and reaction are involved with different activities, time scales Level N Level 2 Level 1 Level 0 More Reactive More Deliberative A Deliberation  Projection Planner Reactor B Behavioral Advice Configurations Parameters B: Planning to guide reaction - configure and set parameters for the reactive control system. C: coupled - concurrent activities PlannerReactor C 11

Example: AuRA R. Arkin (1986)  Planning is viewed as configuration.  Initial A* planner integrated with schema-based controller.  Provides modularity, flexibility, and adaptability. LearningUser Input Plan Recognition User Profile User Intentions Spatial LearningSpatial Goals Opportunism ( סתגלנות ) On-line Adaptation Teleautonomy Mission Alterations Mission Planner Spatial Reasoner Plan Sequencer RE PRE SEN TA TI ON Schema Controller Motor Perceptual ActuationSensing Hierarchical Component Reactive Component 23

Example: Atlantis  E. Gat (1991)  Three layers: controller, sequencer, deliberator.  Asynchronous, heterogeneous: reactivity and deliberation  Planning as advice giving, not as command.  Tested on NASA rovers. Control SensorsActuators Deliberative Sequencing Results ActivationStatus Invocation 26

Example: AuRA R. Arkin (1986)  Planning is viewed as configuration.  Initial A* planner integrated with schema-based controller.  Provides modularity, flexibility, and adaptability. LearningUser Input Plan Recognition User Profile User Intentions Spatial LearningSpatial Goals Opportunism ( סתגלנות ) On-line Adaptation Teleautonomy Mission Alterations Mission Planner Spatial Reasoner Plan Sequencer RE PRE SEN TA TI ON Schema Controller Motor Perceptual ActuationSensing Hierarchical Component Reactive Component 23

Example: Planner-Reactor D. Lyons (1992)  Continuous modification of a reactive control system (sub-optimal).  Planning is a form of reactor adaptation.  Adaptation is on-line rather than off-line deliberation.  Planning is used to remove performance errors when they occur.  Uses Robot Schema (RS) model.  Tested in both assembly cell and grasp planning. Goals Planner World Adaptation Perceptions Reactions REACTOR Action Sensing Perception 29

Planner-Reactor Adaptation: a) a reactor executes under a set of operating assumptions. b) if any assumptions are violated, the planner modifies the reactor’s control system to remove the violation. –Each assumption has a monitor associated with it during run time to ensure its validity. Reactor Performance with Monitoring Reactor Adapted by Planner and Assumptions Relaxed. Assumptions violation detected. Adapt Reactor Restore Initial Reactor. Violation assumptions Restored. Completed Initial Reactor Construction Adapt ReactorStart Execution Normal Performance 31

 Georgeff and A. Lansky (1987)  PRS = Procedural Reasoning System  Reactivity refers to postponement of the elaboration of plans until it is necessary:  a least commitment strategy.  Tested on SRI Flakey Example: PRS INTERPRETER ACTUATORSSENSORS MONITOR BELIEFS DESIRESPLANS INTENTIONS COMMAND GENERATOR OPERATOR INTRFACE 32