Quantum Robots QUANTUM ROBOTICS IN ROBOT THEATRE Quantum

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Quantum Robots

Quantum Robots

QUANTUM ROBOTICS IN ROBOT THEATRE

QUANTUM ROBOTICS IN ROBOT THEATRE

Quantum Signals and Automata Binary Logic 0, 1 Fuzzy Logic [0, 1] Quantum Logic

Quantum Signals and Automata Binary Logic 0, 1 Fuzzy Logic [0, 1] Quantum Logic Hilbert Space, Bloch Sphere

Quantum Signals and Automata Logic circuit Quantum array Finite State Machine Quantum state Machine

Quantum Signals and Automata Logic circuit Quantum array Finite State Machine Quantum state Machine Algorithm Quantum Algorithm

A “Quantum Robot” Concept Quantum Computing Since 1999 Quantum Robotics Intelligent Robotics Since 2004

A “Quantum Robot” Concept Quantum Computing Since 1999 Quantum Robotics Intelligent Robotics Since 2004 Since 1969

Quantum Robotics Constraint Satisfaction Model for Grover Algorithm Collaboration: Martin Lukac, Michitaka Kameyama, Tohoku

Quantum Robotics Constraint Satisfaction Model for Grover Algorithm Collaboration: Martin Lukac, Michitaka Kameyama, Tohoku University, Vamsi Parasa, Erik Paul Quantum Robot Vision Quantum Fuzzy Logic Arushi Raghuvanshi Michael Miller, Univ. Victoria, BC Quantum Braitenberg Vehicles Quantum Emotions Quantum Initialization and Neural Networks Siddhar Manoj Arushi Raghuvanshi Martin Lukac David Rosenbaum

The first quantum robot in the world?

The first quantum robot in the world?

Our concept of quantum robot based on reducing all problems to constraint satisfaction solved

Our concept of quantum robot based on reducing all problems to constraint satisfaction solved on a quantum computer

Orion Quantum Adiabatic Computer in Vancouver BC, Canada Orion interface Personal computer PC Bluetooth

Orion Quantum Adiabatic Computer in Vancouver BC, Canada Orion interface Personal computer PC Bluetooth connection CUDA/GPU supercomputer The whole proposed PSU Quantum Robot system

QUBOT-1 – the world’s first quantum robot

QUBOT-1 – the world’s first quantum robot

Constraints Satisfaction Problems S E N D + M O R E M O

Constraints Satisfaction Problems S E N D + M O R E M O N E Y Graph coloring Cryptographic Problems

Grover Algorithm Reminder in new light

Grover Algorithm Reminder in new light

Graph Coloring • • Building oracle for graph coloring is a better explanation of

Graph Coloring • • Building oracle for graph coloring is a better explanation of Grover than database search. This is not an optimal way to do graph coloring but explains well the principle of building oracles. The Graph Coloring Problem 1 3 2 5 4 Color every node with a color. Every two nodes that share an edge should have different colors. Number of colors should be minimum 1 3 2 5 4 6 6 7 7 This graph is 3 -colorable

Simpler Graph Coloring Problem 1 3 2 We need to give all possible colors

Simpler Graph Coloring Problem 1 3 2 We need to give all possible colors here 4 Two wires for color of node 1 Two wires for color of node 2 Two wires for color of node 3 Two wires for color of node 4 Gives “ 1” when nodes 1 and 2 have different colors 1 2 1 3 2 3 2 4 3 4 F(x) Value 1 for good coloring

Oracle for Quantum Map of Europe Coloring Germany France Switzerland Spain France Germany Switzerland

Oracle for Quantum Map of Europe Coloring Germany France Switzerland Spain France Germany Switzerland Multiple. Valued Quantum Circuits Quaternary qudits Good coloring

Constraint Satisfaction Problem is to satisfy the constraints and minimize the energy Germany France

Constraint Satisfaction Problem is to satisfy the constraints and minimize the energy Germany France Switzerland Spain France Constraints to be satisfied Germany Switzerland Good coloring Energy to be minimized Count number of ones Adiabatic Quantum Computer

Simpler Graph Coloring Problem Give Hadamard for each wire to get superposition of all

Simpler Graph Coloring Problem Give Hadamard for each wire to get superposition of all state, which means the set of all colorings We need to give all possible colors here |0> |0> H H H Discuss naïve nonquantum circuit with a full counter of minterms 1 2 1 3 2 3 2 4 3 4 f(x) Value 1 for good coloring Now we will generate whole Kmap at once using quantum properties - Hadamard

What Grover algorithm does? • Grover algorithm looks to a very big Kmap and

What Grover algorithm does? • Grover algorithm looks to a very big Kmap and tells where is the -1 in it. 1 1 1 1 1 1 1 -1 1 1 1 1 1 1 1 1 Here is -1

“Classical” Quantum Computer Circuit Model for Graph Coloring Simplified schematic of our Graph Coloring

“Classical” Quantum Computer Circuit Model for Graph Coloring Simplified schematic of our Graph Coloring Oracle. We designed 35 oracles

Hadamard Transform Single qubit = = 1/2 1 1 1 -1 1 1 -1

Hadamard Transform Single qubit = = 1/2 1 1 1 -1 1 1 -1 -1 1 Here I calculated Kronecker product of two Hadamards H H H Parallel connection of two Hadamard gates is calculated by Kronecker Product (tensor product)

 • As we remember, these are transformations of Hadamard gate: Motivating calculations for

• As we remember, these are transformations of Hadamard gate: Motivating calculations for 3 variables |0> H |0> + |1> H |0> - |1> In general: |x> H |0> + (-1) x |1> For 3 bits, vector of 3 Hadamards works as follows: |abc> (|0>+(-1)a|1>) (|0>+(-1)b|1>) (|0>+(-1)c|1>) = From multiplication |000> +(-1)c |001> +(-1)b |001>+(-1)b+c |001>000> +(-1)a |001> + (-1)a+c |001> + (-1)a+b |001> (-1)a+b+c |001> If a = b = c =0 then all phases positive

|0> oracle |000> +|001> + |010>+|011> +|100> + |101> +|110> + |111> |0> We

|0> oracle |000> +|001> + |010>+|011> +|100> + |101> +|110> + |111> |0> We can say that Hadamard gates before the oracle create the Kmap of the function, setting the function in each of its possible minterms (cells) in parallel This is like a Kmap with every true minterm (1) encoded by -1 And every false minterm (0) encoded by 1 f(x)

Block Diagram for graph coloring and similar problems Vector Of Basic States |0> All

Block Diagram for graph coloring and similar problems Vector Of Basic States |0> All good colorings are encoded by negative phase Vector Of Hadamards Oracle with Comparators, Global AND gate Think about this as a very big Kmap with -1 for every good coloring Output of oracle Work bits

A practical Example • This presentation shows clearly how to perform a so called

A practical Example • This presentation shows clearly how to perform a so called 1 in 4 search • We start out with the basics 1 in 4 search

Pick your needle and I will find you a haystack The point of this

Pick your needle and I will find you a haystack The point of this slide is to show examples of 4 different oracles. Grovers search can tell between these oracles in a single iteration, classically we would need 3 iterations.

Properties of the oracle Let f : {0, 1}2 {0, 1} have the property

Properties of the oracle Let f : {0, 1}2 {0, 1} have the property that there is exactly one x {0, 1}2 for which f (x) = 1 Goal: find x {0, 1}2 for which f (x) = 1 Classically: 3 queries are necessary Quantumly: ? Only after 3 tests can we determine with certainty that the oracles is 1 for only a single input value x

A 1 -4 search can chose between 4 oracles in one iteration Black box

A 1 -4 search can chose between 4 oracles in one iteration Black box for 1 -4 search: x 1 x 2 y f(x 1, x 2) Start by creating phases in superposition of all inputs to f: 0 0 1 H H H f Input state to query: ( 00 + 01 + 10 + 11 )( 0 – 1 ) Output state: ((– 1) f(00) 00 + (– 1) f(01) 01 + (– 1) f(10) 10 + (– 1) f(11) 11 )( 0 – 1 ) Here we clearly see the Kmap encoded in phase – the main property of many quantum algorithms

This slide illustrates how the state of the system is changed as it propagates

This slide illustrates how the state of the system is changed as it propagates through the quantum network implementation of Grovers Search algorithm. Time 0 0 1 state = 0 1 0 0 0 ab c 0 1 00 01 11 10 1 H H X X H H f H H X X H state = 0. 353 -0. 353 ab c 0 1 0. 3 – 0, 3 00 01 0. 3 – 0, 3 10 0. 3 – 0, 3 state = 0. 353 -0. 353 0. 353 ab c 0 1 00 0. 3 – 0, 3 01 0. 3 – 0, 3 11 - 0. 3 0, 3 10 0. 3 – 0, 3 state = -0. 353 -0. 353 ab c 0 1 state = 0 0 -0. 5 -0. 5 0 0 H H M M H M state = 0 0 -0. 5 0 0 0. 5 -0. 5 ab c 0 1 00 0. 3 – 0, 3 00 - 0. 3 0, 3 01 0. 3 – 0, 3 11 - 0. 3 0, 3 11 0. 3 - 0, 3 10 0. 3 – 0, 3 ab c 0 1 00 0 0 01 - 0. 5 0, 5 11 0 0 11 0. 5 - 0. 5 10 0. 5 – 0, 5 10 0 0

Time 0 0 1 H H f X X H H state = 0.

Time 0 0 1 H H f X X H H state = 0. 353 -0. 353 ab c 0 1 H X X Ibverters flip between 00 and 11 ab c 0 1 00 0. 3 – 0, 3 00 - 0. 3 0, 3 01 0. 3 – 0, 3 11 - 0. 3 0, 3 11 0. 3 - 0, 3 10 0. 3 – 0, 3 Hadamard addis in 00 and 11 ab c 0 1 state = -0. 353 -0. 353 state = 0 0 -0. 5 -0. 5 0 0 Inverter flips second bit when first is 1 ab c 0 1 00 0 0 01 - 0. 5 0, 5 11 0 0 11 0. 5 - 0. 5 10 0. 5 – 0, 5 10 0 0 state = -0. 353 -0. 353 state = 0 0 -0. 5 0 0 0. 5 -0. 5 H H M M H M state = 0 0 -1 state = -0. 353 -0. 353 Ibverters flip between 00 and 11 state = 0 0 1 Hadamard of affine function ab c 0 1 00 -0. 3 01 0. 3 -0. 3 11 -0. 3 10 0. 3 -0. 3 00 - 0. 3 01 0. 3 - 0. 3 11 - 0. 3 10 0. 3 - 0. 3 ab c 0 1 00 01 11 10 -1

Grover Loop Time 0 0 1 H H f H H X X H

Grover Loop Time 0 0 1 H H f H H X X H H H M M H M Inversion about the mean ψ00 = – 00 + 01 + 10 + 11 ψ01 = + 00 – 01 + 10 + 11 ψ10 = + 00 + 01 – 10 + 11 ψ11 = + 00 + 01 + 10 – 11 The state corresponding to the input to the oracle that has a output result of 1 is ‘tagged’ with a negative 1. We need to repeat the Grover Loop N times After Hadamard the solution is “known” in Hilbert space by having value -1. But it is hidden from us This was a special case where we could transform the state vector without repeating the oracle. In general we have to repeat the oracle – general Grover Loop

Measurements Hadamards Constants Grover Loop Quantum Braitenberg Vehicle LEARNING Outputs - actuators Measurements Grover

Measurements Hadamards Constants Grover Loop Quantum Braitenberg Vehicle LEARNING Outputs - actuators Measurements Grover Loop Worst case quadratic speedup on every problem that you can build an oracle! Outputs - actuators Controlled Hadamards Constants Inputs- sensors Control Measurements Inputs- sensors Oracle or Quantum Circuit Grover Search New Concept of Real-time Quantum Search Future work

Our concept of quantum robot based on reducing all problems to constraint satisfaction solved

Our concept of quantum robot based on reducing all problems to constraint satisfaction solved on a quantum computer

Robot Obstacle Avoidance Problem Robot Communication Problem Constraint Satisfaction Problem Robot Reasoning Problem Robot

Robot Obstacle Avoidance Problem Robot Communication Problem Constraint Satisfaction Problem Robot Reasoning Problem Robot Vision Problem New Approach to Quantum Robotics Adiabatic Quantum Computer Classical quantum computing

Orion Quantum Adiabatic Computer in Vancouver BC, Canada Complete multiprocessor classical robot Real quantum

Orion Quantum Adiabatic Computer in Vancouver BC, Canada Complete multiprocessor classical robot Real quantum computer Orion interface Personal computer PC CUDA/GPU supercomputer The whole proposed PSU Quantum Robot system Powerful quantum computer simulator

SAT as a constraint satisfaction problem (a + b’ + c) * (b +

SAT as a constraint satisfaction problem (a + b’ + c) * (b + d’) … = 1 =1 =1 Highly parallel system of updating nodes c a =0 =1 b d (a + b’ + c) (b + d’) Yes , do nothing No , update to nodes

SAT as a constraint satisfaction problem (a + b’ + c) * (b +

SAT as a constraint satisfaction problem (a + b’ + c) * (b + d’) … = 1 Constraints: (a + b’ + c) = 1 (b + d’) = 1 …. . Energy optimization: (a + b’ + c) = f 1 (b + d’) = f 2 …. Min ( f 1’ + f 2’ + …. . ) Orion programming is just writing equations for constraints and equations for energy

Constraint Satisfaction for Robotics • Insufficient speed of robot image processing and pattern recognition.

Constraint Satisfaction for Robotics • Insufficient speed of robot image processing and pattern recognition. – This can be solved by special processors, DSP processors, FPGA architectures and parallel computing. • Prolog allows to write CSP programs very quickly. • An interesting approach is to formulate many problems using the same general model. • This model may be predicate calculus, Satisfiability, Artificial Neural Nets or Constraints Satisfaction Model. – Constraints to be satisfied (complex formulas in general) – Energies to be minimized (complex formulas)

Constraint satisfaction model in robotics Used in main areas of robotics: – vision, –

Constraint satisfaction model in robotics Used in main areas of robotics: – vision, – knowledge acquisition, – knowledge usage. • In particular the following: – planning, scheduling, allocation, motion planning, gesture planning, assembly planning, graph problems including graph coloring, graph matching, floor-plan design, temporal reasoning, spatial and temporal planning, assignment and mapping problems, resource allocation in AI, combined planning and scheduling, arc and path consistency, general matching problems, belief maintenance, experiment planning, satisfiability and Boolean/mixed equation solving, machine design and manufacturing, diagnostic reasoning, qualitative and symbolic reasoning, decision support, computational linguistics, hardware design and verification, configuration, realtime systems, and robot planning, implementation of non-conflicting sensor systems, man-robot and robot-robot communication systems and protocols, contingency-tolerant motion control, multirobot motion planning, multi-robot task planning and scheduling, coordination of a group of robots, and many others

 • End of this part

• End of this part

Additional Slides on Quantum and emotional robotics

Additional Slides on Quantum and emotional robotics

 • Huffman and Clowes created an approach to polyhedral scene analysis, scenes with

• Huffman and Clowes created an approach to polyhedral scene analysis, scenes with opaque, trihedral solids, next improved significantly by Waltz • Popularized the concept of constraints satisfaction and its use in problem solving, especially image interpretation. • Objects in this approach had always three plane surfaces intersecting in every vertex. Constraint Satisfaction Image Analysis by Waltz

Constraint Satisfaction Image Analysis by Waltz • There are only four ways to label

Constraint Satisfaction Image Analysis by Waltz • There are only four ways to label a line in this blocks world model. • The line can be convex, concave, a boundary line facing up and a boundary line facing down (left, or right). • The direction of the boundary line depends on the side of the line corresponding to the face of the causing it object. • Waltz created a famous algorithm which for this world model which always finds the unique correct labeling if a figure is correct.

AC-3: State 2 3. Queue: (2, 3)(3, 2)(3, 4)(4, 3)(4, 1)(1, 4) (1, 3)(3,

AC-3: State 2 3. Queue: (2, 3)(3, 2)(3, 4)(4, 3)(4, 1)(1, 4) (1, 3)(3, 1) 4. Removing (2, 3). 5. L 3 on 2 inconsistent with 3, so it is removed. 6. Of arcs (k, 2), (1, 2) is not on queue, so it is added.

Examples of CSP in robotics • Scene recognition • Motion generation in presence of

Examples of CSP in robotics • Scene recognition • Motion generation in presence of constraints – internal (low power, don’t hit itself) – external (shape of racing track, wolf-man-cabbage-goat) • Gesture under emotions • Communication in a swarm of robots (graph coloring) • Robot guard (set covering)

Adiabatic Quantum Computing to solve Constraint Satisfaction Problems efficiently

Adiabatic Quantum Computing to solve Constraint Satisfaction Problems efficiently

Adiabatic Quantum Computing to solve Constraint Satisfaction Problem efficiently. • Will February 13 th

Adiabatic Quantum Computing to solve Constraint Satisfaction Problem efficiently. • Will February 13 th 2007 be remembered in annals of computing. ? • DWAVE company demonstrated their Orion quantum computing system in Computer History Museum in Mountain View, California. • The first time in history a commercial quantum computer was presented. • The Orion system is a hardware accelerator designed to solve in principle a particular NPcomplete problem called the two-dimensional Ising model in a magnetic field (for instance quadratic programming). • It is built around a 16 -qubit superconducting adiabatic quantum computer (AQC) processor.

Orion computer from DWAVE • Conventional front end • The solution of an NPcomplete

Orion computer from DWAVE • Conventional front end • The solution of an NPcomplete problem: • 1. Pattern matching applied to searching databases of molecules. • 2. Planning/scheduling application for assigning people to seats subject to constraints. • 3. Sudoku 7 3 3 2 4 5 2 4 2 1 8 3 9 5 7 4 6 8 6 9 8 6 2 5 7

Orion Is the Constraint Satisfaction Solver Does it have quadratic speedup? • The company

Orion Is the Constraint Satisfaction Solver Does it have quadratic speedup? • The company promises to provide free access by Internet to one of their systems to those researchers who want to develop their own applications.

Orion computer from DWAVE • The plans are that by the end of year

Orion computer from DWAVE • The plans are that by the end of year 2008 the Orion systems will be scaled to more than 1000 qubits • Company plans to build in 2009 processors specifically designed for quantum simulation, which represents a big commercial opportunity. • These problems include: protein folding, drug design and many other in chemistry, biology and material science. • Thus the company claims to dominate enormous markets of NP -complete problems and quantum simulation.

We plan to concentrate on robotic applications of the Constraint Satisfaction Model. • Adiabatic

We plan to concentrate on robotic applications of the Constraint Satisfaction Model. • Adiabatic Quantum Computing was proved equivalent to standard QC circuit model. • Each of the developed by us methods can be transformed to an adiabatic quantum program and run on Orion. • We developed logic minimization methods to reduce the graph that is created in AQC to program problems such as Maximum Clique or SAT. • This programming is like on “assembly level” but with time more efficient methods will be developed in our group. – This is also similar to programming current Field-Programmable Gate Arrays.

Future work on Adiabatic Quantum Controller for a robot • In the second research/development

Future work on Adiabatic Quantum Controller for a robot • In the second research/development direction the interface to Orion system will be learned • How to formulate front-end formulations for various robotic problems as for various robotic problems constraint-satisfaction problems for this system?

New Research Direction • New approach to quantum robotics based on reduction to Constraint

New Research Direction • New approach to quantum robotics based on reduction to Constraint Satisfaction Model • Well-known problems • New problems

 Quantum Emotional Robots

Quantum Emotional Robots

Emotional Robot Helpers • Because humans attribute emotions to other humans and to animals,

Emotional Robot Helpers • Because humans attribute emotions to other humans and to animals, future emotional robots should perhaps be visually similar to humans or animals, – otherwise their users would be not able to understand robots’ emotions and correctly communicate with them. • Observe that the whole idea of emotional robot helpers is to enable easy communication between humans and robots.

Robot emotions The research on robot emotions and methods to allow humanoid robots to

Robot emotions The research on robot emotions and methods to allow humanoid robots to acquire complex motor skills is recently advancing at a very fast pace. • Simple emotions like “fear” or “anger” or behaviors like obstacle-avoidance for wheeled mobile robots. • Subsumption architecture. • Practically insufficient to cover all necessary behaviors of future household “helper robots”.

 • Larger biped robots are very expensive – hundreds thousands dollars. • Recent

• Larger biped robots are very expensive – hundreds thousands dollars. • Recent small humanoid robots. • We acquired two KHR-1 robots and integrated them to our robot theatre system with its various capabilities such as: – – sensors, vision, speech recognition and synthesis Common Robot Language. Emotions can be best expressed by a biped robot with human-like face

 • Humanoid robots to express emotions: • M. Lukac uses humanlike faces and

• Humanoid robots to express emotions: • M. Lukac uses humanlike faces and head/neck body combinations. • KAIST theatre used wholebody stationary robots with hands. • Walking biped robot can express the fullness of human emotions: – – body gestures, dancing, jumping, gesticulating with hands. • Emotions can be: – Emergent - Arushi – Programmed – Martin Lukac ISMVL – Mimicked – ULSI – Learned – Martin Lukac Reed-Muller

Synthesis of quantum circuits and state machines from examples – Quantum mappings – Quantum

Synthesis of quantum circuits and state machines from examples – Quantum mappings – Quantum Braitenberg Vehicles – Arushi ISMVL 2007 – Quantum Oracles such as Grover – Yale ISMVL 2007 – Emotional State Machines – Lukac ISMVL 2007 – Quantum Automata and Cellular Quantum Automata – Lukac ULSI 2007 – Motion – Quay and Scott

Quantum Emotional Facial Gestures

Quantum Emotional Facial Gestures

First View: Emotion as synthesized behavior synthesized Serchuk et al (2006) discuss emotion as

First View: Emotion as synthesized behavior synthesized Serchuk et al (2006) discuss emotion as mapping from internal state to observable output behavior. We want to design these mappings well, so that they wil be similar to humans Emotional state = state of all emotion variables Physical variables = positions, speeds, accelerations, words,

Wheel of emotions Active - Passive Positive Negative Internal representation of emotions by vectors

Wheel of emotions Active - Passive Positive Negative Internal representation of emotions by vectors in multi-dimensional space Mapping from internal to external representation of emotions

Second View: Emotion as emergent, evolvable behavior • Here emotion is an emergent behavior

Second View: Emotion as emergent, evolvable behavior • Here emotion is an emergent behavior that arises from sensors, drives, effectors and logic. D e g re es of f re e d o m • This may look like human, animal behavior but also as an entirely new “other world” behavior, behavior as it may be. Sensors, vision and fusion = features and patterns Drives and effectors Main input-output mapping (perception, internal state, behavior) Precise motion generation (behavior) Evolved “emotional” behavior of robot

Human Emotions Perceived by Robot • Robot perceives emotions of a human • Emotional

Human Emotions Perceived by Robot • Robot perceives emotions of a human • Emotional aspect of speech • Text from speech recognition • (I hate you example) • Facial gestures • Body language and hand upper body gestures. • Camera with software • Microphones with speech recognition/speech analysis system You do not need robot, this may be done by laptop with microphone and camera.

Robot perceives human mood • From top to bottom, the continua shown in each

Robot perceives human mood • From top to bottom, the continua shown in each row are… – happiness (H) - surprise (U) – surprise (U) - fear (F) – fear (F) - sadness (S) – sadness (S) - disgust (D) – disgust (D) - anger (A) – and anger (A) - happiness (H)

Why we need Robot-Generated Emotions? • Robot presents its emotions to a human •

Why we need Robot-Generated Emotions? • Robot presents its emotions to a human • Why we need it? • Robot who helps elderly • Assistive robot for disabled • Robot that works with mentally challenged children (autism, Asperger Syndrome, ADD), • Robot receptionist • Robot barman • Robot astronaut helper • Robot museum guide • Robot theatre (mostly in interactive theatres) • • Imitation of human emotions Interaction with human based on emotion Improvisation of theatrical plays, texts, stories Interpretation of human behavior in psychological terms, negotiation and cheating

Emotions in Humanoid Robots • Humanoid Robotics focuses on communication with humans that includes:

Emotions in Humanoid Robots • Humanoid Robotics focuses on communication with humans that includes: – – Behavioral changes and emotional expressions, Emotional alterations of text-to-speech, Facial mimics and gestures, Overall body language (posture) and hand upper body gestures (hands, neck). – Member postures and movements 66

Symmetry of emotion transmission Robot reconstructs Human emotion Human behavior Human perceives robot behavior

Symmetry of emotion transmission Robot reconstructs Human emotion Human behavior Human perceives robot behavior and emotion Two aspects – two approaches Robot perceives Human behavior Robot creates its emotion Robot expresses its behavior Emotions as emergent behaviors Emotions as learned behaviors

Traditional and modern theories of emotion • Observable (traditional) emotions: emotional behaviors, moods, content

Traditional and modern theories of emotion • Observable (traditional) emotions: emotional behaviors, moods, content changes (speech variations, etc) • Modern Hypothesis: emotions and feelings are influencing decision making, problem solving, memory efficiency and so on. 68

Two level representation of the Cognitive-Emotional robot Structure Flow of emotions Flow of actions

Two level representation of the Cognitive-Emotional robot Structure Flow of emotions Flow of actions

Quantum Mechanics to model emotions

Quantum Mechanics to model emotions

Quantum Mechanics to Model Emotions • The problem being considered here is the synthesis

Quantum Mechanics to Model Emotions • The problem being considered here is the synthesis of logic controller allowing the robot to modify its actions and express unique emotional states • The emotional expression is desired to be compelling the human user to communicate with the robot, – the behaviors should be original and non-repeating • Standard classical approaches can be compared to an FSM approach; – the robot action space (behavioral space) is a finite set of states that the robot learns or just uses in a input driven mapping 71

Concept: Emotional Quantum State Machine • Design a machine that will simulate the articulation

Concept: Emotional Quantum State Machine • Design a machine that will simulate the articulation of human social behavior: – Subjective – Non repetitive – Innovative • But still: – Socially acceptable or not – Behaviorally understandable – Safe (the framework of this behavior is purely virtual – no contact) 72

Quantum Hierarchical Model of Emotions • Because the concept of emotional expression can be

Quantum Hierarchical Model of Emotions • Because the concept of emotional expression can be extended to a functional model; emotional expression affects the robot functioning. • Here the concept of QFSM is extended to a Quantum Cellular Automata based on the quantum emotional state machine • The quantum string rewriting is extended to a complete robot hierarchy rewriting schema 73

One Machine Model in Our Approach

One Machine Model in Our Approach

Definition of Emotion • Emotion is the result of measurements of a hybrid classical/quantum

Definition of Emotion • Emotion is the result of measurements of a hybrid classical/quantum system • In terms of quantum mechanics, emotions are represented by quantum states that we (observe) know only after measurement, – but we can operate on them deterministically in the Hilbert space. • Emotional evolution is represented by quantum operator (unitary and non-unitary, including the operator measurement)

Simulating emotions for practical applications • Simulating emotions as only observable behaviors is not

Simulating emotions for practical applications • Simulating emotions as only observable behaviors is not sufficient to make emotional robots • Definition: Emotional State Machine is a model of FSM that can modify Definition its state and output independently of the content of the input, but based solely on its current state. • Definition: Robotic Emotions are simulated emotional states allowing the robot to perform a given action in a way that satisfies its current emotional state. • We propose a emotional model as computational process distributed across the robot software controller allowing to use emotions to modify all robot actions 76

Emotional Quantum Automata • • A network of Emotional State Machines is called an

Emotional Quantum Automata • • A network of Emotional State Machines is called an Emotional Quantum Automata (plural - per similarity to Cellular Automata) This network can be regular or not. • If regular, it can be: – – • One-dimensional and one-directional (pipelined) One-dimensional (like one-dimensional cellular automata) Two-dimensional (like Game of Life and Two-Dimensional cellular Automata of Wolfram) more dimensional. Emotional Quantum Automaton is therefore a generalization of Cellular Automata, Random Boolean Networks and Quantum Cellular Automata Example of onedimensional and one-directional Cellular Quantum Automata

Complex space vs. Real States Neighbors Emotional states travels across the robot body Time

Complex space vs. Real States Neighbors Emotional states travels across the robot body Time Simulation of a quantum emotional automata. The dots represents real states (observables) and the surrounding represents complex components. Of interest is the fact that even in a variants that the automata communicate exclusively via classical data channels, the complex part can travel through space. Thus, emotions encoded by such a complex state can be moving across robot controller and create unexpected local effects 78

Emotional Parameters for control in Cynthea Robot Device Parameters Other This slide shows which

Emotional Parameters for control in Cynthea Robot Device Parameters Other This slide shows which parameters are affected by which input and output devices

Emotional Model Emotional Robot controller Emotional Robot sensors Emotional Robot actuators Formal Language •

Emotional Model Emotional Robot controller Emotional Robot sensors Emotional Robot actuators Formal Language • Each element in the robot is represented as Quantum Emotional State Machine (EQSM), such that on each level of robot control hierarchy the emotions can influence both the visible (perceptible) and the nonvisible robot processes 80

Emotional Model • Energy – simulated energy representing the emotional state of the robot

Emotional Model • Energy – simulated energy representing the emotional state of the robot Emotional State Energy • Strategy – the translation function mapping the emotional state to a state parameters, (function dependent) Emotional State Parameters • The input command - represents the robot command such as one obtained from a sensor (user input, other robot), specified in the CRL language • The emotional parameters translated to particular variables, are parameters used to modify the global state of the robot and also the local function (Command rewriting) Emotional Parameters State of the robot 81

Project Overview • KHR-1 – Biped robot – 17 servos – 2 RCB-1 servo

Project Overview • KHR-1 – Biped robot – 17 servos – 2 RCB-1 servo controllers (each 12 servos) – Serial port connectivity

Common Robot Language. • We developed symbolic approach to robot specification based on a

Common Robot Language. • We developed symbolic approach to robot specification based on a Common Robot Language. • While the syntax of this language specifies rules for generating sentences, the semantic aspects describe structures for interpretation. • Every movement is described on many levels, for instance every joint angle or face muscle are at low level and complete movements such as pushups or joyful hand waving are at a high level.

Common Robot Language. • These aspects serve to describe interaction with environment at various

Common Robot Language. • These aspects serve to describe interaction with environment at various levels of description. • It uses also the constraint satisfaction problem creating movements that specify constraints of time, space, motion style and emotional expression.

Describing movements, behaviors and emotions • The goal of our Common Robot Language is

Describing movements, behaviors and emotions • The goal of our Common Robot Language is to describe human-oriented movements • But it exceeds these behaviors to those like anthropomorphic animals and fairy tale characters. • We created new GUI interface and robot controlling language specific to KHR-1. – Editing functions. – Testing functions. – The ability to read information back from the robot by serial communication was added. • There are two main functions that we achieved: – mimicking, – behavior state machine.

 Using HBP robot vision software for human mimicking. • Control behaviors mimicked from

Using HBP robot vision software for human mimicking. • Control behaviors mimicked from a human standing in front of the camera. – (with state machine or not) • We wanted the KHR-1 to mimic human motion that was being shown on the screen by the HBP software. • The HPB works by taking an image of a person’s upper body. It then will try and identify the face. • Once it can recognize a face it will then look at the body. • The image that it acquires is converted to a set of feature (parameters) values assigned to several groups of variables.

What is wrong with our vision software? • • HBP is slow OPENCV is

What is wrong with our vision software? • • HBP is slow OPENCV is slow Robot responds with delay HBP is not accurate • That one great thing about HPB, is that you have the option of modifying the original code to some extent and make your own features. • To speed up the image recognition we will use the Orion quantum computer in the next project

Genetic Algorithm for Motion: Chromosome • The chromosome is an array of 0’s, 1’s,

Genetic Algorithm for Motion: Chromosome • The chromosome is an array of 0’s, 1’s, and -1’s. These values control the 12 servos that dictate the movement of the robot. ( -1 = full left, 1 = full right, 0 = middle position). • Each chromosome holds 4 sets of servo commands to the 12 Servos. So, each chromosome is 48 bits long. • These 4 sets of commands to the servos will generate a sequence of movements that forms a gesture.

Genetic Algorithm: Chromosome • Below is a visual representation of the 48 bit chromosome

Genetic Algorithm: Chromosome • Below is a visual representation of the 48 bit chromosome • Each rows each represent a command to the 12 servos. The 4 rows together will send 4 commands to the 12 servos that will form a gesture • Orange colors sections = Right arm control bits, Blue color sections = Hand control bits, Green color sections = Head control bits

Research Topics

Research Topics

Research areas 1. Quantum Braitenberg Robots 2. Quantum Subsumption Architecture 3. Quantum Emotional Robotics

Research areas 1. Quantum Braitenberg Robots 2. Quantum Subsumption Architecture 3. Quantum Emotional Robotics 4. Other quantum robot architectures such as probabilistic 5. Quantum Search 6. Quantum Image Processing and Pattern Recognition 7. Quantum Spectral Transforms 8. Quantum Games 9. Quantum Theorem Proving 10. Quantum Learning (QNN, Quantum Automata, Markov Models, Bayesian Networks, Associative Memories, etc. 11. Quantum Holographic memories 12. Models of Quantum Physical Processes in biology, chemistry, etc.

Disclaimer – do not worry! • We talk here about emotions of these: And

Disclaimer – do not worry! • We talk here about emotions of these: And not these Words such as “memory, emotion, knowledge, remember, solve, prove” carry humanlike meaning but with time we are used to use them in a broader sense

Our Main New Idea: Two Layer Action-Emotion FSM Model Emotions are not something “additional”

Our Main New Idea: Two Layer Action-Emotion FSM Model Emotions are not something “additional” to rational thinking and acting Emotions are intimately intertwined in every process of a robot on any level of hierarchy Simplified model of Emotional State Machine Instead of a hierarchy of state machines we have a hierarchy of Emotional State Machines

Emotional State Machine Measurement of the machine state Deterministic classical physics/compute science world (Turing

Emotional State Machine Measurement of the machine state Deterministic classical physics/compute science world (Turing compatible) Quantum memory Emotional evolution (operator) Quantum (Hilbert Space) (Quantum Turing Machine Compatible)

Quantum computing Basics In quantum computing the system (circuit) is represented in the form

Quantum computing Basics In quantum computing the system (circuit) is represented in the form of a wave: The space of the system is complex Hilbert space H of dimension N. The basis states are orthonormal, for boolean logic: The operations on the system are in the form of Unitary matrices being rotations of the state vectors in the space H: To retrieve the result the system has to be observed or measured. Measurement is an outside operation on the system, and destroys the quantum state. This operation projects the system onto real basis states such as defined above. Because the measurement is completely random, the information is extracted from the collapsed state that has the form of: 95

Quantum Computing Basics (contd. ) Special phenomena can be observed in quantum: ● The

Quantum Computing Basics (contd. ) Special phenomena can be observed in quantum: ● The system can be in superposition (being in all states at the same time) ● The system can be entangled, the outcome of the whole system or of its subparts is dependent on the measured output qubit(s). Despite the fact that before measurement both qubits have the probability of 0. 5 of being in state 0 or 1, after one of the qubit is measured the state of the second one is instantaneously determined 96

Disclaimer: Definition of Emotion • We use, among other concepts, the quantum concepts to

Disclaimer: Definition of Emotion • We use, among other concepts, the quantum concepts to define and use emotions • In our model emotions are formally defined, you can think about them as quantum states or quantum operators. • Then, in this work there is no implication that our “emotions” are related to human emotions – other than that we want to emulate human behavior by a humanoid robot. So what are robotic emotions?

Motivations for Emotional Robotics • Human-Human interaction is highly variable, individual, unique, non-repeating, etc.

Motivations for Emotional Robotics • Human-Human interaction is highly variable, individual, unique, non-repeating, etc. • Emotional Robot, Humanoid Robot • Quantum emotional state machine • Control logic for robotic quantum controllers in order to increase interactivity and quality of communication • Logic synthesis of such circuits is in the middle of this paper

Emotion al Robot

Emotion al Robot

Emotion Recognition versus Emotion Generation

Emotion Recognition versus Emotion Generation

 • This generic situation, where the robot’s behavior is conditioned upon the input

• This generic situation, where the robot’s behavior is conditioned upon the input from the feature detectors connected to the camera, maps to a constraint satisfaction problem as described here. • The way this would work is that the human / camera / robot system would generate optimization and satisfiability problems, to determine how the robot’s effectors should fire, and these problems can be remotely solved using Orion. • For example, you could acquire a Hansen Robotics Einstein, sit it him on your desk, train a camera on your face, use an anger feature detector that causes the Einstein robot to laugh harder the angrier you get.

Moral Robot

Moral Robot

Concepts of a moral robot 1. So far, moral robots are built for military

Concepts of a moral robot 1. So far, moral robots are built for military (immoral robots? ) 2. Our concept is a moral robot , helper, housekeeper and entertainer for an old demanding lady. 1. 2. 3. Path planning and tasks execution Access to internet Conversation and dancing 1. Robot integrates modal and deontic logic. 2. Principles of morality: 1. 2. 3. 4. Be nice Help and protect Never harm by accident or mistake Resolve conflicts of the lady with family and friends 3. Asimov’s Laws. 4. Exclusions and special cases.

Multiple-Valued Logic in Quantum Domain

Multiple-Valued Logic in Quantum Domain

Oracle for Quantum Map of Europe Coloring A B 0 0 0 1 2

Oracle for Quantum Map of Europe Coloring A B 0 0 0 1 2 3 1 0 3 2 2 3 0 1 3 2 1 0 A B 0 1 2 3 0+1=1 1+1=0 2+1=3 3=1=2 0+0=0 1+0=1 2+0=2 3+0=3 0+3=3 1+3=2 2+3=1 3+3=0 0+2=2 1+2=3 2+2=0 3+2=1 A +1 +1 0 1 B 0 1 +2 2 +3 3 +2 3 Quaternary Feynman 1 when A=B Quaternary input/binary output comparator of equality

Oracle for Quantum Map of Europe Coloring A B Comparator for each frontier +1

Oracle for Quantum Map of Europe Coloring A B Comparator for each frontier +1 0 +3 2 D 1 -- for controls 0, 2 and 3 3 +2 C 1 1 -- when control 1 1 +1 Binary qudit =1 for frontier AB when countries A and B have different colors 0 1 +3 2 3 +2 0 Quaternary controlled binary target gate Binary Toffoli Binary signal 1 when all frontiers well colored

Conclusions and future work. Didactic Aspects • KHR-1 is now able to mimic upper

Conclusions and future work. Didactic Aspects • KHR-1 is now able to mimic upper body human motions. • Students who work on this project learn about robot kinematics, robot vision, state machines (deterministic, non-deterministic, probabilistic and quantum - entangled) robot software programming and commercial robot movement editors. • The most important lesson learned is the integration of a non-trivial large system and the appreciation of what is a real-time programming. • It is important that the students learn to develop a “trial and error” attitude and also how to survive using a non-perfect and incomplete documentation. • It was also emphasized by the professor that students create a very good documentation of their work for the next students to use.

Conclusions 1. Our goals are to both create a model innovative robot theatre and

Conclusions 1. Our goals are to both create a model innovative robot theatre and a theory of robot theatre that theory of robot theatre would be similar to theory of film or theory of interactive computer games. 2. We believe that robot theatre will become a new art form and we are interested what are the basic questions related to the art of performing robots. 3. We hope to have an interesting feedback to our ideas from all groups of PSU researchers.

New Research Area or only application? 1. 2. 3. 4. What is Robot Theatre

New Research Area or only application? 1. 2. 3. 4. What is Robot Theatre Theory? What are its main methods? How to evaluate Robot Theatres? Is robot theatre only an application of robotics or is it more? What ? • We see that humans can laugh looking at our theatre. Will we ever experience humans crying at robot performances? What can we learn from robot theatre that is not a standard robotics problem? • •

1. Final Fundamental Questions: Will Robot theatre be ever as popular art form as

1. Final Fundamental Questions: Will Robot theatre be ever as popular art form as film or theatre? 2. Will robots be popularly used in theatres? 3. Will we see robot theatre toys? 4. Will home robots become also robot entertainers?

Constraint Satisfaction for Robotics • Insufficient speed of robot image processing and pattern recognition.

Constraint Satisfaction for Robotics • Insufficient speed of robot image processing and pattern recognition. – This can be solved by special processors, DSP processors, FPGA architectures and parallel computing. • Prolog allows to write CSP programs very quickly. • An interesting approach is to formulate many problems using the same general model. • This model may be predicate calculus, Satisfiability, Artificial Neural Nets or Constraints Satisfaction Model. – Constraints to be satisfied (complex formulas in general) – Energies to be minimized (complex formulas)

SAT as a constraint satisfaction problem (a + b’ + c) * (b +

SAT as a constraint satisfaction problem (a + b’ + c) * (b + d’) … = 1 =1 =1 Highly parallel system of updating nodes c a =0 =1 b d (a + b’ + c) (b + d’) Yes , do nothing No , update to nodes

SAT as a constraint satisfaction problem (a + b’ + c) * (b +

SAT as a constraint satisfaction problem (a + b’ + c) * (b + d’) … = 1 Constraints: (a + b’ + c) = 1 (b + d’) = 1 …. . Energy optimization: (a + b’ + c) = f 1 (b + d’) = f 2 …. Min ( f 1’ + f 2’ + …. . ) Orion programming is just writing equations for constraints and equations for energy

Conditional robot response based on camera input • This one is really cool. •

Conditional robot response based on camera input • This one is really cool. • At a high level the way it works is as follows. • You have a camera trained on a human. The data taken by the camera is processed so as to detect features, which are generalized patterns of behavior. • For example, a feature detector could be configured to detect the presence of anger in the human, for example by learningbased methods. • In addition, there is a robot, which is connected to the data processing system connected to the camera. • This robot has effectors which control its actions. • In this application, the effector controls are functions of the processed input from the camera, where the rules connecting the two are user-determined.

High Level Schematic of DIM robot

High Level Schematic of DIM robot

Many research and teaching areas My robotics classes and topics of theses are much

Many research and teaching areas My robotics classes and topics of theses are much broader than in other universities: 1. Classical Robotics 2. Speech synthesis and analysis 3. Robot vision 1. 2. 4. 5. 6. 7. Motion generation Dialog and natural language Scripts and agents. Search and Machine Learning. 1. 2. 3. 4. 8. Face recognition and tracking, gesture recognition, etc. Search theory Decision trees and constructive induction (AC decomposition) CSP Pattern Recognition New models: 1. 2. Probabilistic robotics, Quantum Robotics,

Many partial practical projects, broad range of technical knowledge 1. 2. 3. 4. 5.

Many partial practical projects, broad range of technical knowledge 1. 2. 3. 4. 5. 6. 7. 8. 9. Controllers and processors FPGA and PLD, VHDL and Verilog. CUDA and GPU Motors and sensors Robot. C, C, C++, Prolog, LISP, PYTHON, etc. Writing requirements Creativity and invention. Integration of engineering, science and art. Many awards for high school students.

Conclusions 1. Our goals are to both create a model innovative robot theatre and

Conclusions 1. Our goals are to both create a model innovative robot theatre and a theory of robot theatre that would be similar to the robot theatre theory of film or theory of interactive computer games. 2. We believe that robot theatre will become a new art form and we are interested what are the basic questions related to the art of performing robots. 3. We hope to have an interesting feedback to our ideas from the System Science oriented

Questions: 1. Will Robot theatre be ever as popular art form as film or

Questions: 1. Will Robot theatre be ever as popular art form as film or theatre? 2. Will robots be popularly used in theatres? 3. Will we see robot theatre toys? 4. Will home robots be also entertainers?