Intelligent Systems

Lecture 13

Intelligent robots

Classification of robots

Industrial

Classification of robots on purpose

For researchHome and officeMilitarySearching

- welding- painting- loading- unloading- transport- assembling

- For space- For cracks

- For land reconnaissance-aerial reconnaissance- For land tactic operations- For air tactic operations- For space - For underwater operations

- robots-toys- For care of children- For care of elders- robots-guards-multi-purpose home robots

-robots for soccer- Battle robots- For research of behavior and cognition- For research of navigation and planning

Classification of robots on mechanics

1) Robots-manipulator 2) Robots-carts 3) Insect-like robots with legs 4) Robots-pets - Dogs - Cats 5) Humanoid robots 6) Special robots: - Robots-snakes - Soccer players - Space stations - Air robots - Robots-ships - Underwater robots 7) Nanorobots

Classification of robots

• Mobile• Stationary

• Programmed (without AI)

• Learned (without AI)• Learned (with AI)• Self-learning (with

AI)1) Logical (symbol) control system; 2) Neural network based control system; 3) Hybrid control system.

Functions of control system of robot

• Perception and recognition of entities of environment

• Interaction with human• Planning and replanning of behavior• Navigation, control of goal-seeking

behavior• Control of engines (motors)• Learning, forming of model of environment• Interaction with other robots and

equipment

Kinds of learning

• Supervised– Teacher show how system must to answer on input

data (what to do in any situation)

• Unsupervised– System itself finds laws in data

• Reinforcement– System selects behavior on base award obtained

from environment and estimation of state of environment (on base on interaction with environment)

Kinds of planning• Planning systems are problem-solving algorithms that operate on explicit

propositional (or first-order) representations of states and actions. These representations make possible the derivation of effective heuristics and the development of powerful and flexible algorithms for solving problems.

• The STRIPS language describes actions in terms of their preconditions and effects and describes the initial and goal states as conjunctions of positive literals. The ADL language relaxes some of these constraints, allowing disjunction, negation, and quantifiers.

• State-space search can operate in the forward direction (progression) or the backward direction (regression). Effective heuristics can be derived by making a subgoal independence assumption and by various relaxations of the planning problem.

• Partial-order planning (POP) algorithms explore the space of plans without committing to a totally ordered sequence of actions. They work back from the goal, adding actions to the plan to achieve each subgoal. They are particularly effective on problems amenable to a divide-and-conquer approach.

The agent-environment interaction in reinforcement

learning

Features of reinforcement learning and main concepts

• Learning is combined with working• Working is a sequence of actions• Plan of actions is policy • Plan (policy) may be corrected in every time (step)• Action is selected from policy (or no) in according to

estimation of state of environment (or estimation of action in same state) and reward received from environment

• Estimation of environment is determined by goal (target)• Estimation of environment or action is executed with

delay after obtaining of award

Definition of planning

Relationships among learning, planning, and acting

                                                   

Traditional (to 1985) decomposition of a mobile robot control system into functional modules

Brooks: “The key idea from intelligence is:Intelligence is determined by the dynamics of interaction with the world.”

A decomposition of mobile robot control system based on task achieving behavior

Principles formulated by Brooks (1991) for behavior-based robots

• There is no central model maintained of the world. All data is distributed over many computational elements

• There is no central locus of control• There is no separation into perceptual system, central system, and actuation

system. Pieces of the network may perform more than one of these functions. More importantly, there is intimate intertwining of aspects of all three of them.

• The behavioral competence of the system is improved by adding more behavior-specific network to the existing network. We call this process layering. This is a simplistic and crude analogy to evolutionary development. As with evolution, at every stage of the development the systems are tested-unlike evolution there is a gentle debugging process available. Each of the layers is a behavior-producing piece of network in its own right, although it may implicitly rely on presence of earlier pieces of network.

• There is no hierarchical arrangement, i.e., there is no notion of one process calling on another as a subroutine. Rather the networks are designed so that needed computations will simply be available on the appropriate input line when needed. There is no explicit synchronization between a producer and a consumer of messages. Message reception buffers can be overwritten by new messages before the consumer has looked at the old one. It is not atypical for a message producer to send 10 messages for every one that is examined by the receiver.

• The layers, or behaviors, all run in parallel. There may need to be a conflict resolution mechanism when different behaviors try to give different actuator commands.

• The world is often a good communication medium for processes, or behaviors, within a single robot.

Tasks and features of humanoid robots• Being a mobile robot with power supply and computer

control on-board• Navigating and moving in an environment made for

humans• Biped walking in a humanoid style• Gripping and manipulating objects designed for humans• Cooperative working with humans• Interacting with humans without endangering their safety• Having autonomous behavior• Communicating with humans in a simple and intuitive

way• Using a stereo-vision system as main sensor system• Using learning and adaptive behavior strategies• Using human-like intelligence• Having a design pleasing to real humans

Architecture of control system

Functional structure of control system

Features of control systems of Sony’s robots

• Adaptive control of movement in real time

• Selection of kind of gait (walk) in real time

• Possibility of perception of space of real world in real time

• Multi-modal interaction with human

Main behavior systems of dog, investigated by Sony during

development of Aibo

Internal motivational variables

Modules within Defense-Escape mode

Modes comprising Agonistic Subsystem

Role of Drives in Behavior Selection

Connection of emotion with behavior

Using of emotions in selection of behavior

Objects used in experiments:

• Meat (red)

• Water (blue)

• Owner (green)

Selection of behavior

Tree of behaviors

Architecture of EGO

Storing and using of association between visual image and its name

Architecture of humanoid robots of Sony

Recognition of multi-faces

Emotion-based behavior of robot SDR-4X