Advanced Topics in Robotics CS493/790 (X) Lecture 2 Instructor: Monica Nicolescu

CS 493/790(X) - Lecture 22 Robot Components Sensors Effectors and actuators –Used for locomotion and manipulation Controllers for the above systems –Coordinating information from sensors with commands for the robot’s actuators Robot = an autonomous system which exists in the physical world, can sense its environment and can act on it to achieve some goals

CS 493/790(X) - Lecture 23 Sensors Sensor = physical device that provides information about the world –Process is called sensing or perception Robot sensing depends on the task Sensor (perceptual) space –All possible values of sensor readings –One needs to “see” the world through the robot’s “eyes” –Grows quickly as you add more sensors

CS 493/790(X) - Lecture 24 State Space State: A description of the robot (of a system in general) State space: All possible states a robot could be in –E.g.: light switch has two states, ON, OFF; light switch with dimmer has continuous state (possibly infinitely many states) Different than the sensor/perceptual space!! –Internal state may be used to store information about the world (maps, location of “food”, etc.) How intelligent a robot appears is strongly dependent on how much and how fast it can sense its environment and about itself

CS 493/790(X) - Lecture 25 Representation Internal state that stores information about the world is called a representation or internal model –Self: stored proprioception, goals, intentions, plans –Environment: maps –Objects, people, other robots –Task: what needs to be done, when, in what order Representations and models influence determine the complexity of a robot’s “brain”

CS 493/790(X) - Lecture 26 Action Effectors: devices of the robot that have impact on the environment (legs, wings  robotic legs, propeller) Actuators: mechanisms that allow the effectors to do their work (muscles  motors) Robotic actuators are used for –locomotion (moving around, going places) –manipulation (handling objects)

CS 493/790(X) - Lecture 27 Autonomy Autonomy is the ability to make one’s own decisions and act on them. –For robots: take the appropriate action on a given situation Autonomy can be complete (R2D2) or partial (teleoperated robots)

CS 493/790(X) - Lecture 28 Control Architectures Robot control is the means by which the sensing and action of a robot are coordinated Controllers enable robots to be autonomous –Play the role of the “brain” and nervous system in animals Controllers need not (should not) be a single program –Typically more than one controller, each process information from sensors and decide what actions to take –Should control modules be centralized? Challenge: how do all these controllers coordinate with each other?

CS 493/790(X) - Lecture 29 Spectrum of robot control From “Behavior-Based Robotics” by R. Arkin, MIT Press, 1998

CS 493/790(X) - Lecture 210 Robot control approaches Reactive Control – Don’t think, (re)act. Deliberative (Planner-based) Control – Think hard, act later. Hybrid Control – Think and act separately & concurrently. Behavior-Based Control (BBC) – Think the way you act.

CS 493/790(X) - Lecture 211 Thinking vs. Acting Thinking/Deliberating –slow, speed decreases with complexity –involves planning (looking into the future) to avoid bad solutions –thinking too long may be dangerous –requires (a lot of) accurate information –flexible for increasing complexity Acting/Reaction –fast, regardless of complexity –innate/built-in or learned (from looking into the past) –limited flexibility for increasing complexity

CS 493/790(X) - Lecture 212 Reactive Control : Don’t think, react! Technique for tightly coupling perception and action to provide fast responses to changing, unstructured environments Collection of stimulus-response rules Limitations –No/minimal state –No memory –No internal representations of the world –Unable to plan ahead –Unable to learn Advantages –Very fast and reactive –Powerful method: animals are largely reactive

CS 493/790(X) - Lecture 213 Deliberative Control : Think hard, then act! In DC the robot uses all the available sensory information and stored internal knowledge to create a plan of action: sense  plan  act (SPA) paradigm Limitations –Planning requires search through potentially all possible plans  these take a long time –Requires a world model, which may become outdated –Too slow for real-time response Advantages –Capable of learning and prediction –Finds strategic solutions

CS 493/790(X) - Lecture 214 Hybrid Control : Think and act independently & concurrently! Combination of reactive and deliberative control –Reactive layer (bottom): deals with immediate reaction –Deliberative layer (top): creates plans –Middle layer: connects the two layers Usually called “three-layer systems” Major challenge: design of the middle layer –Reactive and deliberative layers operate on very different time-scales and representations (signals vs. symbols) –These layers must operate concurrently Currently one of the two dominant control paradigms in robotics

CS 493/790(X) - Lecture 215 Behavior-Based Control : Think the way you act! Behaviors: concurrent processes that take inputs from sensors and other behaviors and send outputs to a robot’s actuators or other behaviors to achieve some goals An alternative to hybrid control, inspired from biology Has the same capabilities as hybrid control: –Act reactively and deliberatively Also built from layers –However, there is no intermediate layer –Components have a uniform representation and time-scale

CS 493/790(X) - Lecture 216 Fundamental Differences of Control Time-scale: How fast do things happen? –how quickly the robot has to respond to the environment, compared to how quickly it can sense and think Modularity: What are the components of the control system? –Refers to the way the control system is broken up into modules and how they interact with each other Representation: What does the robot keep in its brain? –The form in which information is stored or encoded in the robot

CS 493/790(X) - Lecture 217 Behavior Coordination Behavior-based systems require consistent coordination between the component behaviors for conflict resolution Coordination of behaviors can be: –Competitive: one behavior’s output is selected from multiple candidates –Cooperative: blend the output of multiple behaviors –Combination of the above two

CS 493/790(X) - Lecture 218 Competitive Coordination Arbitration: winner-take-all strategy  only one response chosen Behavioral prioritization –Subsumption Architecture Action selection/activation spreading (Pattie Maes) –Behaviors actively compete with each other –Each behavior has an activation level driven by the robot’s goals and sensory information Voting strategies –Behaviors cast votes on potential responses

CS 493/790(X) - Lecture 219 Cooperative Coordination Fusion: concurrently use the output of multiple behaviors Major difficulty in finding a uniform command representation amenable to fusion Fuzzy methods Formal methods –Potential fields –Motor schemas –Dynamical systems

CS 493/790(X) - Lecture 220 Fusion:  flocking (formations) Example of Behavior Coordination Arbitration:  foraging (search, coverage)

CS 493/790(X) - Lecture 221 How to Choose a Control Architecture? For any robot, task, or environment consider: –Is there a lot of sensor noise? –Does the environment change or is static? –Can the robot sense all that it needs? –How quickly should the robot sense or act? –Should the robot remember the past to get the job done? –Should the robot look ahead to get the job done? –Does the robot need to improve its behavior and be able to learn new things?

CS 493/790(X) - Lecture 222 Learning & Adaptive Behavior Learning produces changes within an agent that over time enable it to perform more effectively within its environment Adaptation refers to an agent’s learning by making adjustments in order to be more attuned to its environment –Phenotypic (within an individual agent) or genotypic (evolutionary) –Acclimatization (slow) or homeostasis (rapid)

CS 493/790(X) - Lecture 223 Learning Learning can improve performance in additional ways: Introduce new knowledge (facts, behaviors, rules) Generalize concepts Specialize concepts for specific situations Reorganize information Create or discover new concepts Create explanations Reuse past experiences

CS 493/790(X) - Lecture 224 Learning Methods Reinforcement learning Neural network (connectionist) learning Evolutionary learning Learning from experience –Memory-based –Case-based Learning from demonstration Inductive learning Explanation-based learning Multistrategy learning

CS 493/790(X) - Lecture 225 Reinforcement Learning (RL) Motivated by psychology (the Law of Effect, Thorndike 1991): Applying a reward immediately after the occurrence of a response increases its probability of reoccurring, while providing punishment after the response will decrease the probability One of the most widely used methods for adaptation in robotics

CS 493/790(X) - Lecture 226 Reinforcement Learning Goal: learn an optimal policy that chooses the best action for every set of possible inputs Policy: state/action mapping that determines which actions to take Desirable outcomes are strengthened and undesirable outcomes are weakened Critic: evaluates the system’s response and applies reinforcement –external: the user provides the reinforcement –internal: the system itself provides the reinforcement (reward function)

CS 493/790(X) - Lecture 227 Learning to Walk Maes, Brooks (1990) Genghis: hexapod robot Learned stable tripod stance and tripod gait Rule-based subsumption controller Two sensor modalities for feedback: –Two touch sensors to detect hitting the floor: - feedback –Trailing wheel to measure progress: + feedback

CS 493/790(X) - Lecture 228 Learning to Walk Nate Kohl & Peter Stone (2004)

CS 493/790(X) - Lecture 229 Supervised Learning Supervised learning requires the user to give the exact solution to the robot in the form of the error direction and magnitude The user must know the exact desired behavior for each situation Supervised learning involves training, which can be very slow; the user must supervise the system with numerous examples

CS 493/790(X) - Lecture 230 Neural Networks One of the most used supervised learning methods Used for approximating real-valued and vector- valued target functions Inspired from biology: learning systems are built from complex networks of interconnecting neurons The goal is to minimize the error between the network output and the desired output –This is achieved by adjusting the weights on the network connections

CS 493/790(X) - Lecture 231 ALVINN ALVINN (Autonomous Land Vehicle in a Neural Network) Dean Pomerleau (1991) Pittsburg to San Diego: 98.2% autonomous

CS 493/790(X) - Lecture 232 Learning from Demonstration & RL S. Schaal (’97) Pole balancing, pendulum-swing-up

CS 493/790(X) - Lecture 233 Learning from Demonstration Inspiration: Human-like teaching by demonstration DemonstrationRobot performance

CS 493/790(X) - Lecture 234 Learning from Robot Teachers Transfer of task knowledge from humans to robots Human demonstrationRobot performance

CS 493/790(X) - Lecture 235 Classical Conditioning Pavlov 1927 Assumes that unconditioned stimuli (e.g. food) automatically generate an unconditioned response (e.g., salivation) Conditioned stimulus (e.g., ringing a bell) can, over time, become associated with the unconditioned response

CS 493/790(X) - Lecture 236 Darvin’s Perceptual Categorization Two types of stimulus blocks –6cm metallic cubes –Blobs: low conductivity (“bad taste”) –Stripes: high conductivity (“good taste”) Instead of hard-wiring stimulus-response rules, develop these associations over time Early trainingAfter the 10 th stimulus

CS 493/790(X) - Lecture 237 Genetic Algorithms Inspired from evolutionary biology Individuals in a populations have a particular fitness with respect to a task Individuals with the highest fitness are kept as survivors Individuals with poor performance are discarded: the process of natural selection Evolutionary process: search through the space of solutions to find the one with the highest fitness

CS 493/790(X) - Lecture 238 Genetic Operators Knowledge is encoded as bit strings: chromozome –Each bit represents a “gene” Biologically inspired operators are applied to yield better generations

CS 493/790(X) - Lecture 239 Evolving Structure and Control Karl Sims 1994 Evolved morphology and control for virtual creatures performing swimming, walking, jumping, and following Genotypes encoded as directed graphs are used to produce 3D kinematic structures Genotype encode points of attachment Sensors used: contact, joint angle and photosensors

CS 493/790(X) - Lecture 240 Evolving Structure and Control Jordan Pollak –Real structures

CS 493/790(X) - Lecture 241 Readings F. Martin: Sections 1.1, 1.2.3, 5 M. Matarić: Chapters 1, 3, 10