ROBOTICS 01PEEQW Basilio Bona DAUIN – Politecnico di Torino

Mobile & Service Robotics Supervision and control

Traditional approach Traditional artificial intelligence considers robot “brains” as sequential processing units Basilio Bona - DAUIN - PoliTo3 ROBOTICS 01PEEQW /2016 Main ideas  Representation -> reasoning -> planning  Model building (maps of the environment is required)  Functional decomposition; hierarchical systems  Symbolic/semantic manipulation

Supervision and Control Basilio Bona - DAUIN - PoliTo4 ROBOTICS 01PEEQW /2016 Localization & Map Building Path planning & Reasoning Position Global map Environment Data treatment Sensors Data treatment Actuators data Perception Motion control a priori knowledgeTask/mission commands commands iteration

Supervision and Control Basilio Bona - DAUIN - PoliTo5 ROBOTICS 01PEEQW /2016 Localization Map Building Path planning Reasoning Position Global map Perception Motion control Environment Local map World model Path

Control Strategies  Control loop requirements  World changes dynamically  A compact model of the world is very difficult to define  There are many sources of uncertainty, both in the world and in the robot  Two possible approaches + a combination of them deliberative Classic AI – deliberative model  Approximate world modeling (model-based method)  Based on a set of functions  Vertical decomposition  Top-down approach reactive Modern AI – reactive model  No world model is given: behavior-based control  Horizontal decomposition  Bottom-up approach Basilio Bona - DAUIN - PoliTo6 ROBOTICS 01PEEQW /2016

Control Characteristics Basilio Bona - DAUIN - PoliTo7 ROBOTICS 01PEEQW /2016 This architecture may produce a slow and delayed response senseuse modelplanact Vertical approach Function 1 Function 2 Function 3 Function 5 Function 4 Sense – Plan – Act Subsumption/Reactive model Horizontalapproach

Model-Based Approach  Requires the complete modeling of the world  Each block is a computed function  Vertical decomposition and nested-embodiment of functions An example Perception Cognitive planning Localization - Map building Motion control sensorsactuators Basilio Bona - DAUIN - PoliTo ROBOTICS 01PEEQW /2016 8

Model-Based Approach Basilio Bona - DAUIN - PoliTo9 ROBOTICS 01PEEQW /2016 Low level control Planner Navigator Path monitor Pilot Controller GOAL RECOGNITION GLOBAL PATH PLANNING SUB-GOAL FORMULATION LOCAL PATH PLANNING TARGET GENERATOR DYNAMIC PATH PLANNING TARGET LOCATION PATH CORRECTION/OBSTACLE AVOIDANCE COMMANDS SENSORS ACTUATORS

Behavior-Based Approach Basilio Bona - DAUIN - PoliTo10 ROBOTICS 01PEEQW /2016

Behavior-Based Approach Rodney Brooks, inventor of this approach, writes:  Complex behavior needs not necessarily be the product of a complex control system  The world is its best model  Simplicity is a virtue  Intelligence is in the eye of the observer  Robots should be cheap  Robustness in the presence of noisy or failing sensors is a design goal  Planning is just a way of avoiding figuring out what to do next  All onboard computation is important  Systems should be built incrementally  No representation. No calibration. No complex computers. No high band communication Basilio Bona - DAUIN - PoliTo ROBOTICS 01PEEQW /

Behavior-Based Approach Various names given to this approach  Subsumption architecture  Reactive system  Reflexive behavior  Perception-action Basilio Bona - DAUIN - PoliTo12 ROBOTICS 01PEEQW /2016 WORLD Perception 1Perception 2Action 1Action 2

Subsumption architecture  The subsumption architecture was originally proposed by Brooks [1986]  The subsumption architecture copies the synergy between sensation and actuation in lower animals such as insects  Brooks argues that instead of building complex agents in simple worlds, we should follow the evolutionary path and start building simple agents in the real, complex and unpredictable world  From this argument, a number of key features of subsumption result Basilio Bona - DAUIN - PoliTo ROBOTICS 01PEEQW /

Subsumption architecture 1.No explicit knowledge representation is used. Brooks often refers to this as “The world is its own best model” 2.Behavior is distributed rather than centralized 3.Response to stimuli is reflexive – the perception-action sequence is not modulated by cognitive deliberation 4.The agents are organized in a bottom-up fashion. Thus, complex behaviors are fashioned from the combination of simpler, underlying ones 5.Individual agents are inexpensive, allowing a domain to be populated by many simple agents rather than a few complex ones. These simple agents individually consume little resources (such as power) and are expendable, making the investment in each agent minimal 6.Several extensions have been proposed to pure reactive subsumption systems. These extensions are known as behavior- based architectures Basilio Bona - DAUIN - PoliTo ROBOTICS 01PEEQW /

Behavior-Based Approach Characteristics  No model is necessary  Horizontal decomposition  Coordination + Priority = Fusion  Biomimesis = observe and copy animal behavior  Subsumption  Embodiment Basilio Bona - DAUIN - PoliTo ROBOTICS 01PEEQW / Avoid obstacles Discover new areas Detect goal position Communicate data Recharge Follow right/left wall sensorsactuators Coordination / fusion

Behavior-Based Approach Basilio Bona - DAUIN - PoliTo16 ROBOTICS 01PEEQW /2016

Behavior-Based Approach Basilio Bona - DAUIN - PoliTo17 ROBOTICS 01PEEQW /2016

Behavior-Based Approach Basilio Bona - DAUIN - PoliTo18 ROBOTICS 01PEEQW /2016

Subsumption architecture Basilio Bona - DAUIN - PoliTo19 ROBOTICS 01PEEQW /2016

Subsumption architecture Basilio Bona - DAUIN - PoliTo20 ROBOTICS 01PEEQW /2016

Subsumption architecture Basilio Bona - DAUIN - PoliTo21 ROBOTICS 01PEEQW /2016

Control Strategies: deliberative vs reactive Basilio Bona - DAUIN - PoliTo22 ROBOTICS 01PEEQW /2016 Predictive capabilities Speed of response Depends on accurate world models DELIBERATIVE Model-based REACTIVE Behavior-based Purely symbolic Reflexive Depends on the world representation Slow response High level intelligence (cognition) Variable latency Representation-free Real-time response Low level intelligence (stimulus- response) Fast and easy computation

Embodiment  To embody (verb) = to manifest or personify in concrete form; to incarnate; to incorporate, to unite into one body  Embodiment is the way in which human (or any other animal) psychology arises from the brain & body physiology  Embodiment theory was introduced into AI by Rodney Brooks in the ‘80s. Brooks have claimed that all autonomous agents need to be both embodied and situated  The theory states that intelligent behavior emerges from the interplay between brain, body and world. Brain, body and world are equally important factors in the explanation of how particular intelligent behaviors originate in practice  Brooks showed that robots could be more effective if they “thought” (planned or processed) as little as possible  The robot's intelligence is organized only for handling the minimal amount of information necessary to make its behavior be appropriate and/or as desired by its creator Basilio Bona - DAUIN - PoliTo ROBOTICS 01PEEQW /

Embodiment  Rolf Pfeifer (AILab Zurich) says that there are essentially two directions in artificial intelligence: one focused on developing useful algorithms or robots; and another direction that focuses on understanding intelligence, biological or artificial  In order to make progress on the second direction, an embodied perspective is mandatory Basilio Bona - DAUIN - PoliTo ROBOTICS 01PEEQW /

Situated robot  A situated robot is one which does not deal with abstract representations of the world (which may be simulated or real), but rather reacts directly to its environment as seen through its sensors  An alternative to having a situated robot would be one which builds up a representation of its world and then makes plans based on this representation  Because of the limitations of our present technology, these two approaches often seem contradictory  At present, each approach can be appropriate for different applications Basilio Bona - DAUIN - PoliTo ROBOTICS 01PEEQW /

Situated robot  The situated approach is good for dealing with problems where planning ahead is unnecessary or takes too much time  However, the representation approach is needed for solving more complicated problems where it is necessary to reason about the state of the world  For dealing with complicated tasks in the real world, it will probably be necessary to fuse the two approaches  Reasoning can be used to build up higher level plans and solve high level problems  Lower level functions may use a more situated approach for carrying out plans and dealing with problems which need immediate attention  The structure and relations that originates from the interaction of simple controllers and complex environment is called emergent behavior Basilio Bona - DAUIN - PoliTo ROBOTICS 01PEEQW /

Robotics and AI Main areas of Artificial Intelligence applied to robotics 1.Knowledge representation 2.Understanding natural languages 3.Learning 4.Planning and problem solving 5.Inference 6.Search 7.Vision Basilio Bona - DAUIN - PoliTo ROBOTICS 01PEEQW /

Basilio Bona - DAUIN - PoliTo28 ROBOTICS 01PEEQW /2016

Knowledge representation  Define, build and memorize the physical and virtual structures used by the robot to represent the world the desired tasks itself  Example: a robot is looking after a human being under the wreckage of a fallen building: how it is represented? Structural model:  Head (oval) + trunk (cylindrical) + arms (cylindrical)  Bilateral symmetry Physical model (thermal image, …) What happens if the body is only partially visible? Basilio Bona - DAUIN - PoliTo ROBOTICS 01PEEQW /

Understanding natural languages Basilio Bona - DAUIN - PoliTo30 ROBOTICS 01PEEQW /2016 Natural language is one of the most simple and human ways to interact But … To understand the words does not mean to understand the meaning Grammatical structure vs Semantic structure Example We gave the monkeys two bananas because they were hungry We gave the monkeys two bananas because they were over-ripe They have the same grammatical structure, but a very different semantic structure To understand the sense we must know both the monkeys and the bananas Necessity to develop ontologies

Ontology  An ontology is a formal representation of knowledge as a set of concepts within a domain, and the relationships between those concepts. It is used to reason about the entities within that domain, and may be used to describe the domain  An ontology is a “formal, explicit specification of a shared conceptualization.” An ontology provides a shared vocabulary, which can be used to model a domain — that is, the type of objects and/or concepts that exist, and their properties and relations  Ontologies are the structural frameworks for organizing information and are used in artificial intelligence, etc., as a form of knowledge representation about the world or some part of it Basilio Bona - DAUIN - PoliTo31 ROBOTICS 01PEEQW /2016

Learning  Learning is the capacity to memorize actions and behaviors and to repeat them to adapt to the implicit or explicit objectives  In a broad sense, learning is the ability to adapt during life  We know that most living organisms with a nervous system display some type of adaptation during life  The ability to adapt quickly is crucial for autonomous robots that operate in dynamic and partially unpredictable environments, but the learning systems developed so far have so many constraints that are hardly applicable to robots that interact with an environment without human intervention  Learning requires A structure able to store and retrieve data One or more explicit objectives An adaptation mechanism (reward + punishment) An explicit or implicit teacher Basilio Bona - DAUIN - PoliTo ROBOTICS 01PEEQW /

Planning and problem solving  Intelligence is associated to the ability to plan actions toward the the given task fulfillment, and to solve problems arising when plans fail Go there Basilio Bona - DAUIN - PoliTo ROBOTICS 01PEEQW /

Planning and problem solving Basilio Bona - DAUIN - PoliTo34 ROBOTICS 01PEEQW /2016

SLAM Basilio Bona - DAUIN - PoliTo35 ROBOTICS 01PEEQW /2016 SLAM Simultaneous Localization and Mapping

Inference  Inference is a procedure that allows to generate an answer also when the available data or information are incomplete  Inference is based on statistical models (bayesian networks) or semantic models Basilio Bona - DAUIN - PoliTo ROBOTICS 01PEEQW /

Search  Search does not necessarily mean a true search of objects in space, but defines the ability to examine a knowledge representation data-base (search space) to find the required answer  Consider a computer playing chess: the best move is found looking for a solution in the search space of all possible moves, starting from the present chessboard state Basilio Bona - DAUIN - PoliTo ROBOTICS 01PEEQW /

Vision  Vision is the most important sense in human beings  Psychological studies have demonstrated that the ability to solve problems is due to our brain capacity to visualize the effects of each action Basilio Bona - DAUIN - PoliTo ROBOTICS 01PEEQW /

Books Basilio Bona - DAUIN - PoliTo39 ROBOTICS 01PEEQW /2016 R.C. Arkin Behavior-Based Robotics MIT Press, 1998 R.R. Murphy Introduction to AI Robotics MIT Press, 2000 G. Dudek, M. Jenkin Computational Principles of Mobile Robotics Cambridge U.P., 2000 R. Siegwart, I.R. Nourbakhsh Autonomous Mobile Robots MIT Press, 2004

Books Basilio Bona - DAUIN - PoliTo40 ROBOTICS 01PEEQW /2016 Autori Vari Principles of Robot Motion MIT Press, 2005 S. Thrun, W. Burgard, D. Fox Probabilistic Robotics MIT Press, 2005 Rolf Pfeifer, Josh C. Bongard How the Body Shapes the Way We Think A New View of Intelligence Foreword by Rodney Brooks MIT Press, 2006