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Robots: del autómata al asistente inteligente
El documento explora la evolución de los robots desde sus orígenes hasta la actualidad, definiendo un robot como una máquina programable diseñada para interactuar con su entorno. Detalla la historia del desarrollo robótico, incluyendo hitos importantes y la clasificación de robots según su autonomía y capacidades de aprendizaje. También aborda conceptos de robots adaptativos, evolutivos y sociables, centrándose en su interacción con los humanos y su capacidad para adaptarse y aprender.
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Robots: del autómata al asistente inteligente
- 1. Robots: de losautomatas al asistente inteligente Cristina Urdiales Dpto. Tecnología Electrónica Universidad de Málaga
- 2. ¿Qué es unrobot?
- 3. Conceptos generales • Robot(1979): manipulador reprogramable y multifuncional diseñado para llevar a cabo una tarea específica a través de una serie de movimientos programados (Robot Institute of America).
- 4. Conceptos generales • Robot(1990): Máquina controlada por ordenador y programada para moverse, manipular objetos y realizar trabajos a la vez que interacciona con su entorno.
- 5. 1. Breve historia Elmito de Pigmalion. “Sobre el teatro automático” Heron (280 aC) Grecia clásica Falta necesidad y tecnología.
- 6. 1. Breve historia Robot antropomórfico (DaVinci, 1490) Fuente del pavo Real (Al Jazari, 1206) Pato de Vaucanson (Vaucanson , 1750)
- 7. 2. Breve historia 1921: Capek usa por primera vez el término robot en R.U.R. 1926: Fritz Lang presenta por primera vez robots mecánicos en Metropolis.
- 8. 1. Breve historia 1942:Isaac Asimov escribe Runaround y I, Robot. Inicio de la robótica como ciencia.
- 9. 1. Breve historia 1946:Devol y Englenberger dise ñ an los Unimates, que transportan maquinaria. 1946: Presper Eckert y Maulchy dise ñ an el Eniac.
- 10. 1. Breve historia 1948-49:Se desarrolla la tortuga Elsie (Grey Walters)
- 11. 1. Breve historia 1959: Planet Corporation comercializa por primera vez un robot. 1962: General Motors incluye robots en sus cadenas de montaje.
- 12. 2. Breve historia 1960: Shakey (John Hopkins, Stanford)
- 13. 1. Breve historia 1974:Scheinman funda Vicarm. 1976: Las sondas Viking incorporan brazos robóticos.
- 14. 1. Breve historia 1976:Se comienza a desarrollar el Mars Rover. A ñ os 90: Aparecen el COG, el IT, el Lunar Rover, el Dante, el P3, el Soujourner…
- 15. 2. Robots autónomos •Un robot se considera autónomo e inteligente cuando: – El sistema de navegación reside en la propia máquina y es capaz de operar sin conexiones físicas a equipos externos. – Es capaz de tomar decisiones por sí solo a partir de la lectura de sus sensores
- 16. 2. Robots autónomos •El sistema de control de un robot autónomo puede ser: – Deliberados: SPA-primero perciben, luego planifican y, finalmente, actúan. – Reactivos: asocian patrón y acción. Comportamientos más complejos resultan de las interacciones de los sencillos. – Híbridos: combinan los dos anteriores
- 17.
- 18. 3. Robots adaptables •Laadaptación se basa en el aprendizaje, o capacidad de resolver una tarea sin necesidad de programarla explícitamente. •El aprendizaje facilita a los usuarios no expertos a incorporar robots a la vida diaria (~plug&play) •El objetivo final del aprendizaje es decirle al robot QUE tiene que hacer y no COMO hacerlo.
- 19. 3. Robots adaptables Redes neuronales: modelan funciones no lineales a partir de un conjunto de datos Logica difusa: permite modelar conocimiento de forma parecida a la humana Razonamiento basado en casos: Permite aprender a partir de Experiencias previas
- 20. 3. Robots adaptables Ejemplo:Campos de potencial en navegación 1 U q =α⋅d q , goal 2∑ β⋅ i d q , obs i
- 21. 3. Robots adaptables Aprendizajesupervisado • El sistema consiste en “enseñarle” al robot qué haría un humano en su lugar manejándolo con un joystick.
- 22. 3. Robots adaptables Aprendizajepor experiencia • El sistema consiste en “enseñarle” al robot qué haría un humano en su lugar manejándolo con un joystick.
- 23.
- 24. 4. Robots evolutivos •Vida artificial: Sistemas artificiales que exhiben propiedades similares a los seres vivos, a través de modelos de simulación. • Christopher Langton fue el primero en utilizar el término a finales de los años 1980 ("Primera Conferencia Internacional de la Síntesis y Simulación de Sistemas Vivientes“, Los Alamos National Laboratory) • Se define “vida” como una manera de autoreproducción, almacenamiento de información, evolución, crecimiento y adaptación.
- 25. 4. Robots evolutivos COREWARS •Un papel se copia a sí mismo lo más rápidamente, así sacrifica velocidad de ataque por perdurabilidad. •Una piedra directamente bombardea direcciones de memoria intentando matar rápidamente al mayor número de enemigos. •Una tijera son los que emplean estrategias sofisticadas. •Un vampire o pit-trapper roba procesos a sus oponentes alterando su código máquina en el bombardeo para que pierdan ciclos. •Un imp es tan pequeño que su tamaño lo hace difícil de neutralizar.
- 26. 4. Robots evolutivos JUEGO DE LA VIDA •El juego de la vida es el mejor ejemplo de un autómata celular, diseñado por el matemático británico John Horton Conway en 1970. •Permite observar cómo patrones complejos pueden provenir de la implementación de reglas muy sencillas. •Cada célula tiene 8 células vecinas. Las células tienen dos estados: "vivas" o "muertas“: Una célula muerta con exactamente 3 células vecinas vivas "nace" al turno siguiente. Una célula viva con 2 o 3 células vecinas vivas sigue viva, en otro caso muere o permanece muerta("soledad" o "superpoblación") Bloque Barco Parpadeador Sapo Planeador Nave ligera
- 27. 4. Robots evolutivos “Thekey to create intelligent robots consists of letting them evolve, self-organize, and adapt to their environment” Idea (1984): Valentino Braitenberg: “Robots en la mesa” Primer experimento (1994): EPFL, Swiss Federal Institute of Technology in Lausanne y la Universidad de Sussex en Brighton
- 28. 4. Robots evolutivos Experimentos: Predador - Presa
- 29. 4. Robots evolutivos Sistemas biomiméticos
- 30. 4. Robots evolutivos Sistemas cooperativos
- 31. 4. Robots evolutivos Sistemas deformables
- 32. 5. Robots sociables Aefectos de convivir con los humanos, se busca que los robots presenten un comportamiento empático. Para ello, se analiza el comportamiento humano a partir de: expresión facial, postura corporal, gestos, dirección de la mirada y voz.
- 33. 5. Robots sociables Color de piel. Movimiento. Detección de ojos. Estimación de distancia. Aproximación. Estimación de velocidad. Detección de sonido Detección de habla Estimación de tiempos
- 34.
- 35. . 5. Robots sociables . ROBOTS SOCIABLES
- 36. . 5. Robots sociables ROBOTS SOCIABLES
- 37. . 5. Robots sociables . ROBOTS SOCIABLES
Notas del editor
- #28 n order to carry out evolutionary experiments without human intervention, at EPFL we developed the miniature mobile robot Khepera (6 cm of diameter for 70 grams) with eight simple light sensors distributed around its circular body (6 on one side and 2 on the other side) and two wheels (figure 1). Given its small size, the robot could be attached to a computer through a cable hanging from the ceiling and specially designed rotating contacts in order to continuously power the robot and let the computer keep a record of all its movements and neural circuit shapes during the evolutionary process, a sort of fossil record for later analysis. The computer generated an initial population of random artificial chromosomes composed of 0's and 1's that represented the properties of an artificial neural network. Each chromosome was then decoded, one at a time, into the corresponding neural network whose input neurons were attached to the sensors of the robots and the output unit activations were used to set the speeds of the wheels. The decoded neural circuit was tested on the robot for some minutes while the computer evaluated its performance (fitness). In these experiments, we wished to evolve the ability to move straight and avoid obstacles. Therefore, we instructed the computer to select for reproduction those individuals whose wheels moved on a similar direction (straight motion) and whose sensors had lower activation (far from obstacles). Once all the chromosomes of the population had been tested on the robot, the chromosomes of selected individuals were organized in pairs and parts of their genes were exchanged with small random errors in order to generate a number of offspring. These offspring formed a new generation that was again tested and reproduced several times. After 50 generations (corresponding to approximately two days of continuous operation), we found a robot capable of performing complete laps around the maze without ever hitting obstacles. The evolved circuit was rather simple, but still more complex than hand-designed circuits for similar behaviours because it exploited non-linear feedback connections among motor neurons in order to get away from some corners. Furthermore, the robot always moved in the direction corresponding to the higher number of sensors. Although the robot is perfectly circular and could move in both directions in the early generations, those individuals moving in the direction with less sensors tended to remain stuck in some corners because they could not perceive it properly and thus disappeared from the population. This represented a first case of adaptation of neural circuits to the body shape of the robot in a specific environment.
- #29 Two robots, a predator and a prey, are co-evolved within a square arena. The predator has a short-range sensors (1 cm) and a vision system, whereas the prey has only short-range sensors but can go twice as fast. Each robot is tested against the best individuals of the previous 5 generations for 40 seconds. The fitness for the predator was inversely proportional to the time needed to catch the prey, the fitness of the prey instead was proportional to the amount of time it managed to escape the predator. In about 25 generations (a few days of co-evolution on the real robots) we observe good chasing and escaping strategies, such as the one shown in this video sequence. If you observe the two robots for further generations, you can notice a variety of different behaviors. For example, in those generations where the prey moves very fast along the walls, the predator does not attempt to follow it but it simply backs up to a wall and there it waits for the prey that travels too fast to avoid it. We can run co-evolutionary experiments thanks to a set of triple rotating contacts. After 20 generations, the predators developed the ability to search for the prey and follow it while the prey escaped moving all around the arena. However, since the prey could go faster than the predator, this strategy did not always pay off for predators. 25 generations later we noticed that predators watched the prey from far and eventually attacked it anticipating its trajectory. As a consequence, the prey began to move so fast along the walls that often predators missed the prey and crashed into the wall. Again, 25 generations later we discovered that predators developed a "spider strategy". Instead of attempting to go after the prey, they quietly moved towards a wall and waited there for the prey which moved so fast that could not detect the predator early enough to avoid it!
- #30 Two robots, a predator and a prey, are co-evolved within a square arena. The predator has a short-range sensors (1 cm) and a vision system, whereas the prey has only short-range sensors but can go twice as fast. Each robot is tested against the best individuals of the previous 5 generations for 40 seconds. The fitness for the predator was inversely proportional to the time needed to catch the prey, the fitness of the prey instead was proportional to the amount of time it managed to escape the predator. In about 25 generations (a few days of co-evolution on the real robots) we observe good chasing and escaping strategies, such as the one shown in this video sequence. If you observe the two robots for further generations, you can notice a variety of different behaviors. For example, in those generations where the prey moves very fast along the walls, the predator does not attempt to follow it but it simply backs up to a wall and there it waits for the prey that travels too fast to avoid it. We can run co-evolutionary experiments thanks to a set of triple rotating contacts. After 20 generations, the predators developed the ability to search for the prey and follow it while the prey escaped moving all around the arena. However, since the prey could go faster than the predator, this strategy did not always pay off for predators. 25 generations later we noticed that predators watched the prey from far and eventually attacked it anticipating its trajectory. As a consequence, the prey began to move so fast along the walls that often predators missed the prey and crashed into the wall. Again, 25 generations later we discovered that predators developed a "spider strategy". Instead of attempting to go after the prey, they quietly moved towards a wall and waited there for the prey which moved so fast that could not detect the predator early enough to avoid it!
- #31 Two robots, a predator and a prey, are co-evolved within a square arena. The predator has a short-range sensors (1 cm) and a vision system, whereas the prey has only short-range sensors but can go twice as fast. Each robot is tested against the best individuals of the previous 5 generations for 40 seconds. The fitness for the predator was inversely proportional to the time needed to catch the prey, the fitness of the prey instead was proportional to the amount of time it managed to escape the predator. In about 25 generations (a few days of co-evolution on the real robots) we observe good chasing and escaping strategies, such as the one shown in this video sequence. If you observe the two robots for further generations, you can notice a variety of different behaviors. For example, in those generations where the prey moves very fast along the walls, the predator does not attempt to follow it but it simply backs up to a wall and there it waits for the prey that travels too fast to avoid it. We can run co-evolutionary experiments thanks to a set of triple rotating contacts. After 20 generations, the predators developed the ability to search for the prey and follow it while the prey escaped moving all around the arena. However, since the prey could go faster than the predator, this strategy did not always pay off for predators. 25 generations later we noticed that predators watched the prey from far and eventually attacked it anticipating its trajectory. As a consequence, the prey began to move so fast along the walls that often predators missed the prey and crashed into the wall. Again, 25 generations later we discovered that predators developed a "spider strategy". Instead of attempting to go after the prey, they quietly moved towards a wall and waited there for the prey which moved so fast that could not detect the predator early enough to avoid it!
- #32 Two robots, a predator and a prey, are co-evolved within a square arena. The predator has a short-range sensors (1 cm) and a vision system, whereas the prey has only short-range sensors but can go twice as fast. Each robot is tested against the best individuals of the previous 5 generations for 40 seconds. The fitness for the predator was inversely proportional to the time needed to catch the prey, the fitness of the prey instead was proportional to the amount of time it managed to escape the predator. In about 25 generations (a few days of co-evolution on the real robots) we observe good chasing and escaping strategies, such as the one shown in this video sequence. If you observe the two robots for further generations, you can notice a variety of different behaviors. For example, in those generations where the prey moves very fast along the walls, the predator does not attempt to follow it but it simply backs up to a wall and there it waits for the prey that travels too fast to avoid it. We can run co-evolutionary experiments thanks to a set of triple rotating contacts. After 20 generations, the predators developed the ability to search for the prey and follow it while the prey escaped moving all around the arena. However, since the prey could go faster than the predator, this strategy did not always pay off for predators. 25 generations later we noticed that predators watched the prey from far and eventually attacked it anticipating its trajectory. As a consequence, the prey began to move so fast along the walls that often predators missed the prey and crashed into the wall. Again, 25 generations later we discovered that predators developed a "spider strategy". Instead of attempting to go after the prey, they quietly moved towards a wall and waited there for the prey which moved so fast that could not detect the predator early enough to avoid it!