Robot Metaphors and Models Animatronic Robot or device

Robot Metaphors and Models

Animatronic “Robot” or device brain effectors

Perceiving “Robot” sensors brain

Reactive Robot is the simplest behavioral robot sensors Brain is a mapping effectors This is the simplest robot that satisfies the definition of a robot

Reactive Robot in environment ENVIRONMENT is a feedback sensors brain effectors This is the simplest robot that satisfies the definition of a robot

Braitenberg Vehicles and Quantum Automata Robots

Another Example: Braitenberg Vehicles and Quantum BV

Braitenberg Vehicles

Emotional Robot has a simple form of memory or state Brain sensors is a Finite State Machine effectors This is the simplest robot that satisfies the definition of a robot

Behavior as an interpretation of a string • • Newton, Einstein and Bohr. Hello Professor Hello Sir Turn Left. Turn right. behavior

Behavior as an interpretation of a tree • • Newton, Einstein and Bohr. Hello Professor Hello Sir Turn Left. Turn right. behavior Grammar. Derivation. Alphabets.

Our Base Model and Designs

Neck and upper body movement generation

Robot Head Construction, 1999 High school summer camps, hobby roboticists, undergraduates Furby head with new control Jonas We built and animated various kinds of humanoid heads with from 4 to 20 DOF, looking for comical and entertaining values.

Mister Butcher Latex skin from Hollywood 4 degree of freedom neck

Robot Head Construction, 2000 Skeleton Alien We use inexpensive servos from Hitec and Futaba, plastic, playwood and aluminum. The robots are either PC-interfaced, use simple micro-controllers such as Basic Stamp, or are radio controlled from a PC or by the user.

Technical Construction, 2001 Details Adam Marvin the Crazy Robot

Virginia Woolf 2001 heads equipped with microphones, USB cameras, sonars and CDS light sensors

2002 Max BUG (Big Ugly Robot) Image processing and pattern recognition uses software developed at PSU, CMU and Intel (public domain software available on WWW). Software is in Visual C++, Visual Basic, Lisp and Prolog.

Visual Feedback and Learning based on Constructive Induction Uland Wong, 17 years old 2002

2002, Japan Professor Perky with automated speech recognition (ASR) and text -to-speech (TTS) capabilities • We compared several commercial speech systems from Microsoft, Sensory and Fonix. • Based on experiences in highly noisy environments and with a variety of speakers, we selected Fonix for both ASR and TTS for Professor Perky and Maria robots. 1 dollar latex skin from China • We use microphone array from Andrea Electronics.

Maria, 2002/2003 20 DOF

Construction details of Maria location of head servos skull location of controlling rods Custom designed skin location of remote servos

Animation of eyes and eyelids

Cynthia, 2004, June

Currently the hands are not moveable. We have a separate hand design project.

Software/Hardware Architecture • Network- 10 processors, ultimately 100 processors. • Robotics Processors. ACS 16 • Speech cards on Intel grant • More cameras • Tracking in all robots. • Robotic languages – Alice and Cyc-like technologies.

Face detection localizes the person and is the first step for feature and face recognition. Acquiring information about the human: face detection and recognition, speech recognition and sensors.

Face features recognition and visualization.

Use of Multiple. Valued (fivevalued) variables Smile, Mouth_Open and Eye_Brow_Raise for facial feature and face recognition.

HAHOE KAIST ROBOT THEATRE, KOREA, SUMMER 2004 Czy znacie dobra sztuke dla teatru robotow? Sonbi, the Confucian Scholar Paekchong, the bad butcher

Editing movements

Yangban the Aristocrat and Pune his concubine The Narrator

The Narrator

We base all our robots on inexpensive radiocontrolled servo technology.

We are familiar with latex and polyester technologies for faces Martin Lukac and Jeff Allen wait for your help, whether you want to program, design behaviors, add muscles, improve vision, etc.

New Silicone Skins

A simplified diagram of software explaining the principle of using machine learning based on constructive induction to create new interaction modes of a human and a robot.

Probabilistic and Finite State Machines

Probabilistic State Machines to describe emotions “you are beautiful” P=1 / ”Thanks for a compliment” Happy state “you are blonde!” P=0. 7 / Do you suggest I am an idiot? ” Ironic state “you are blonde!” P=0. 3 / ”I am not an idiot” Unhappy state

Facial Behaviors of Maria asks: Response: Do I look like younger than twenty three? §“no” §“yes” 0. 3 Maria smiles §“no” 0. 7 Maria frowns

Probabilistic Grammars for performances Speak ”Professor Perky”, blinks eyes twice P=0. 1 Speak ”Professor Perky” Where? P=0. 3 Who? P=0. 5 Speak ”Doctor Lee” Speak “in some location”, smiles broadly P=0. 5 Speak “In the classroom”, shakes head What? P=0. 1 Speak “Was singing and dancing” P=0. 1 …. P=0. 1 Speak “Was drinking wine”

Human-controlled modes of dialog/interaction “Thanks, I have a lesson” “Hello Maria” Robot performs Human teaches “Question” Robot asks “Stop performance” “Thanks, I have a question” “Questioning finished” Human asks “Lesson finished” “Thanks, I have a command” “Command finished” Human commands

Dialog and Robot’s Knowledge

Robot-Receptionist Initiated Conversation Human Robot What can I do for you? Robot asks This represents operation mode

Robot-Receptionist Initiated Conversation Human Robot What can I do for you? Robot asks I would like to order a table for two

Robot-Receptionist Initiated Conversation Human Robot Smoking or nonsmoking? Robot asks

Robot-Receptionist Initiated Conversation Human Robot Smoking or nonsmoking? Robot asks I do not understand

Robot-Receptionist Initiated Conversation Human Robot Do you want a table in a smoking or non-smoking section of the restaurant? Non-smoking section is near the terrace. Robot asks

Robot-Receptionist Initiated Conversation Human Robot Do you want a table in a smoking or non-smoking section of the restaurant? Non-smoking section is near the terrace. Robot asks A table near the terrace, please

Human-Initiated Conversation Human Robot Hello Maria initialization Robot asks

Human-Initiated Conversation Robot What can I do for you? Robot asks Human Hello Maria

Human-Asking Human Robot Question Robot asks Question Human asks

Human-Asking Robot Yes, you ask a question. Human Question Human asks

Human-Asking Robot Yes, you ask a question. Human What book wrote Lee? Human asks

Human-Asking Robot I have no sure information. Human What book wrote Lee? Human asks

Human-Asking Robot I have no sure information. Human Try to guess. Human asks

Human-Asking Robot Lee wrote book “Flowers”. Human Try to guess. Human asks

Human-Asking Robot Lee wrote book “Flowers”. Human This is not true. Human asks

Human ends questioning Human-Teaching Human Robot Questioning finished Human asks “Questioning finished” Human teaches Robot asks Thanks, I have a lesson

Robot enters asking mode Human-Teaching Human Robot What can I do for you? Questioning finished Human asks “Questioning finished” Human teaches Robot asks Thanks, I have a lesson

Human starts teaching Human-Teaching Human Robot What can I do for you? Thanks, I have a lesson Human asks “Questioning finished” Human teaches Robot asks Thanks, I have a lesson

Human-Teaching Robot Yes Human Thanks, I have a lesson Human teaches

Human-Teaching Robot Yes Human I give you questionanswer pattern Human teaches

Human-Teaching Robot Human Question pattern: Yes What book Smith wrote? Human teaches

Human-Teaching Robot Human Answer pattern: Yes Smith wrote book “Automata Theory” Human teaches

Human-Teaching Robot Human Checking question: Yes What book wrote Smith? Human teaches

Human-Teaching Robot Human Checking question: Smith wrote book “Automata Theory” What book wrote Smith? Human teaches

Human-Teaching Robot Yes Human I give you questionanswer pattern Human teaches

Human-Teaching Robot Human Question pattern: Yes Where is room of Lee? Human teaches

Human-Teaching Robot Human Answer pattern: Yes Lee is in room 332 Human teaches

Human-Checking what robot learned Human Robot Lesson finished Robot asks “Lesson finished” Question Human teaches Human asks

Human-Checking what robot learned Human Robot Lesson finished What can I do for you? Robot asks “Lesson finished” Question Human teaches Human asks

Human-Checking what robot learned Human Robot Question What can I do for you? Robot asks “Lesson finished” Question Human teaches Human asks

Human-Asking Human Robot Yes, you ask a question. Robot asks “Lesson finished” Question Human teaches Human asks

Human-Asking Robot Yes, you ask a question. Human What book wrote Lee? Human asks

Human-Asking Robot I have no sure information. Human What book wrote Lee? Human asks

Human-Asking Robot I have no sure information. Human Try to guess. Human asks

Human-Asking Robot Lee wrote book “Automata Theory” Observe that robot found similarity between Smith and Lee and generalized (incorrectly) Human Try to guess. Human asks

Behavior, Dialog and Learning • The dialog/behavior has the following components: – (1) Eliza-like natural language dialogs based on pattern matching and limited parsing. • Commercial products like Memoni, Dog. Com, Heart, Alice, and Doctor all use this technology, very successfully – for instance Alice program won the 2001 Turing competition. – This is a “conversational” part of the robot brain, based on pattern-matching, parsing and black-board principles. – It is also a kind of “operating system” of the robot, which supervises other subroutines.

Behavior, Dialog and Learning • (2) Subroutines with logical data base and natural language parsing (CHAT). – This is the logical part of the brain used to find connections between places, timings and all kind of logical and relational reasonings, such as answering questions about Japanese geography.

Behavior, Dialog and Learning • (3) Use of generalization and analogy in dialog on many levels. – Random and intentional linking of spoken language, sound effects and facial gestures. – Use of Constructive Induction approach to help generalization, analogy reasoning and probabilistic generations in verbal and non-verbal dialog, like learning when to smile or turn the head off the partner.

Behavior, Dialog and Learning • (4) Model of the robot, model of the user, scenario of the situation, history of the dialog, all used in the conversation. • (5) Use of word spotting in speech recognition rather than single word or continuous speech recognition. • • (6) Continuous speech recognition (Microsoft) • (7) Avoidance of “I do not know”, “I do not understand” answers from the robot. – Our robot will have always something to say, in the worst case, over-generalized, with not valid analogies or even nonsensical and random.