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Ходить как человек: генеративный ИИ и локомоция

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Глядя на улицы города утром буднего дня, мы видим множество людей, каждый из которых торопливо или размеренно идет куда-то по своим делам, будь то на учебу или на работу. Скорость, особенности шага и общая картина локомоции человеческой ходьбы являются уникальными для каждого человека. При этом обстоятельства окружающей среды имеют немалое влияние на то как ходит человек. Говоря о роботах, мы уже давно научили их ходить, подобно человеку. Однако адаптация к динамическим условиям окружающей среды, особенно настройка скорости в реальном времени, остаются крайне сложной задачей. Ученые из Университета Тохоку (Япония) разработали новую методику обучения роботов, использовав возможности генеративного ИИ. Насколько данная методика была эффективной для обучения роботов, и насколько лучше стала их локомоция? Ответы на эти вопросы мы найдем в докладе ученых.

Основа исследования


Антропоморфные роботы являются центром внимания не только писателей-фантастов, но и научных сообществ во всем мире. На данный момент существует множество исследований и разработок из области робототехники, основная задача которых заключается в наделении роботов особенностями, присущим человеку (поведение, внешний вид, манера общения, локомоция и т. д.).

Когда-то роботы были неуклюжи и не могли даже близко сравниться в локомоции с человеком. Даже методы передвижения были иными (колеса, гусеницы и т. д.). Благодаря огромному труду инженеров роботы обзавелись двуногим передвижением, которое не только открыло новое окно возможностей, но и породило множество новых вопросов.

Одной из самых явных проблем двуногого передвижения роботов является адаптация под динамические условия окружающей среды. Некоторые существующие роботы-гуманоиды, в том числе продвинутые модели, такие как Atlas от Boston Dynamics, добились значительного прогресса. Но даже они еще не обладают стопроцентной адаптируемостью. Ученые считают, что усовершенствовать роботов можно с помощью новых методов их обучения.

За последние годы особое внимание приобрел алгоритм машинного обучения, называемый «глубокое обучение с подкреплением» (DRL от deep reinforcement learning). DRL предполагает обучение агента освоению оптимального поведения посредством взаимодействия с окружающей средой методом проб и ошибок, используя сигнал вознаграждения для управления своими действиями. В робототехнике DRL используется для улучшения выполнения различных задач, таких как захват объектов и передвижение, особенно у четвероногих роботов. Однако одним из текущих препятствий в применении DRL к роботам-гуманоидам является большое пространство, которое необходимо исследовать, и дисбаланс двуногого передвижения. Это затрудняет непосредственное обучение желаемой походке, поскольку существует множество возможных комбинаций движений, которые следует учитывать, и многие из них приводят к падениям. Проще говоря, DRL метод обучения может работать эффективно, если учесть все возможные комбинации условий окружающей среды, а также учесть изменчивость этих условий в реальном времени.

Когда речь заходит о каком-либо обучении искусственных систем, ученые часто обращаются за вдохновением к биологическим системам. К примеру, центральный генератор упорядоченной активности (CPG от central pattern generator) является результатом этого вдохновения. CPG — это нейронные цепи, расположенные в спинном мозге, которые генерируют ритмические паттерны мышечной активности, например те, которые используются во время ходьбы и бега. Используя CPG, роботы могут достигать более естественных и стабильных движений, аналогичных движениям живых организмов. Механизм CPG включает сеть взаимосвязанных нейронов, генерирующих колебательные сигналы, которые передаются мышцам, ответственным за движение.

У животных рефлекторная цепь обычно работает вместе с CPG в качестве контроля по принципу обратной связи. Вычислительные модели использовались для исследования слияния CPG с сенсорной обратной связью. Однако вопрос о том, как эффективно интегрировать и применять их для управления передвижением двуногих роботов, остается нерешенным, поскольку CPG потенциально могут ограничить пространство управления и помочь уменьшить размерность, но чтобы быть адаптивными и гибкими для различных сред, они должны хорошо поддерживаться рефлекторными сетями.

Авторы рассматриваемого нами сегодня труда заявляют, что их исследование направлено на улучшение алгоритмов обучения для передвижения роботов с использованием CPG и механизма сенсорной обратной связи. Ученые обучили контроллер CPG, используя имитационное обучение, а затем обучили рефлекторную нейронную сеть, используя DRL. В отличие от других алгоритмов, которые используют обучение с подкреплением для целей имитации, цель рассматриваемого обучения заключалась не только в том, чтобы заставить агента вести себя аналогично собранным данным о движениях человека. Вместо этого использовалось имитационное обучение, чтобы обучить CPG-подобный контроллер формированию управления с прямой связью.

CPG-подобный контроллер был разработан для генерации ритмических паттернов вращающих моментов суставов, аналогичных тем, которые генерируются CPG в живых организмах. Ученые использовали имитационное обучение для формирования шаблонов обучения CPG, чтобы избежать сложных вычислений и настройки, необходимых для других нелинейных функций. Затем рефлекторную нейронную сеть обучили с помощью DRL, чтобы корректировать движения, генерируемые CPG-подобной сетью, на основе сенсорной обратной связи, что позволяет роботу адаптироваться к изменениям в окружающей среде. Важно отметить, что в этом исследовании обучение с подкреплением используется для формирования рефлекторной нейронной сети для поддержки CPG, а не для формирования самого CPG.

Методология обучения


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Изображение №1

Система управления – адаптивная имитируемая CPG (AI-CPG от adaptive imitated CPG) – состоит из следующих элементов (1b):

  • генератор ритма (G), определяющий ритм двигательной активности;
  • слой (S) формирования шаблона, который формирует ритмические сигналы синхронизации в соответствии с целевыми углами суставов робота;
  • PD контроллер, который выводит моторные команды на основе ошибки между текущими углами суставов и целевыми углами суставов;
  • контроллер рефлекторной нейронной сети ® на основе сенсорной обратной связи.

В процессе управления движением робота команда скорости на 1b модулирует скорость робота, изменяя частоту G и S. Это соответствует аналогичному механизму на 1a, где мозг регулирует двигательный паттерн человека путем нисходящей модуляции в спинномозговую сеть. Предыдущие исследования показали, как нисходящая модуляция регулирует активность CPG, взаимодействует с сенсорно-управляемой моделью и облегчает переходы от ходьбы к бегу. G и S служат контроллерами CPG с прямой связью, которые уменьшают размерность пространства действий робота, используя предварительные знания. Напротив, R служит контроллером обратной связи, отвечающим за поддержание баланса робота и адаптацию к данной физической среде.

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Изображение №2

Как показано на 2a и 2b, ученые обучали S посредством имитационного обучения, используя данные о движениях человека из CMU базы данных захвата движений, которая состояла из набора данных о походке как для ходьбы, так и для бега. Ученые использовали быстрое преобразование Фурье (FFT от fast Fourier transform) для получения частот движения fw и fr для двух наборов данных. На основе частот движения и G были рассчитаны входные функции, используемые для обучения. После сопоставления входных функций с реальными данными о движении во временном ряду были получены наборы обучающих данных, которые использовались для обучения S посредством контролируемого обучения. Изменяя частоту (f) входного синусоидального сигнала до S(t, f), ученые могли генерировать углы суставов и крутящие моменты робота-гуманоида, соответствующие различным скоростям движения.

Результаты исследования


Чтобы продемонстрировать возможности разработанной системы управления (AI-CPG), ученые обучили агента-гуманоида выполнять задачу перемещения по прямой линии с разными скоростями на ровной поверхности. Для сравнения были использованы два алгоритма: PPO (от proximal policy optimization) и AMP (от adversarial motion priors). PPO — один из наиболее часто используемых методов DRL в робототехнике. Его преимуществом является обработка многомерных и непрерывных пространств состояний и действий со стабильной производительностью обучения. AMP — это новый алгоритм создания анимации и управления роботами, который сочетает в себе имитационное обучение, состязательное обучение и DRL. Это эффективный метод имитации естественного и реалистичного поведения на основе реальных данных о движении без необходимости искусственного проектирования функций вознаграждения.

Чтобы условия эксперимента при обучении были максимально схожими, количество эпох для каждого из трех алгоритмов составляло 3000, причем каждая эпоха длилась 1000 итераций. Число параллельно обученных актеров составило 8192, а используемые нейронные сети представляли собой MLP со скрытыми слоями размеров для всех трех алгоритмов. Два набора данных реального движения, использованные для обучения AMP, были идентичны тем, которые использовались для обучения AI-CPG.

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Изображение №3

Для алгоритма PPO были установлены целевые скорости в функции вознаграждения равными 1.5, 2.0, 2.5, 3.0, 3.5 и 4.0 м/с. Алгоритм AMP изучает две разные скорости на основе используемых им данных о движении человека при ходьбе и беге. Для AI-CPG диапазон входной частоты генератора CPG составляет [0.8, 1.4] для обучения и [0.7, 1.4] для тестирования. Через каждые 100 эпох обучения нейронная сеть сохранялась в качестве контрольной точки. Каждый алгоритм обучался на пяти случайных начальных числах. Результат обучения показан на изображении выше.

Сравнивая CoT (от cost of transport) при разных средних скоростях на 3a, становится заметным тот факт, что агент, обученный с помощью AI-CPG, мог регулировать скорость своего движения, используя только один контроллер нейронной сети, даже для широкого диапазона скоростей. Кроме того, U-образная зависимость CoT-скорость при ходьбе и линейная зависимость CoT-скорость при беге очень похожи на таковые у человека.

Однако скорость движения робота, обученного с использованием алгоритма PPO, была ограничена структурой его функции вознаграждения. Алгоритм AMP чрезмерно ориентирован на то, чтобы движения робота были похожи на реальные, что затрудняет гибкую настройку скорости движения. Кроме того, по сравнению с алгоритмом AMP, алгоритмы PPO и AI-CPG оптимизировали энергоэффективность робота во время движения, следуя учету энергии в функции вознаграждения.

На 3b показано сравнение индекса симметрии робота на разных скоростях, а на 3e и 3f показана походка робота, движущегося в симуляторе. Было установлено, что, поскольку алгоритм PPO не ссылался на какие-либо реальные данные о движении во время тренировки, симметрия его походки была намного хуже, чем у AMP и AI-CPG. Эти ненормальные походки ограничивают применение метода PPO в реальных роботах.

Как показано на 3c, ученые проверили надежность контроллера походки, приложив определенную высокую случайную силу от -200 Н до 200 Н по всем осям к туловищу агента во время ходьбы. Период переложения внешней силы длился 0.1 секунды каждую секунду движения. В ходе 60-секундного теста было замечено, что для роботов, управляемых по одному и тому же алгоритму, состояние движения с низкой скоростью более нестабильно, чем состояние с высокой скоростью. Более того, у AI-CPG и AMP было меньше падений по сравнению с PPO в большинстве диапазонов скоростей. Это говорит о том, что походка, подобная человеческой, которую AMP и AI-CPG изучают на основе данных о движении человека, более устойчива к внешним воздействиям по сравнению с ненормальной походкой PPO.

На 3d ученые использовали «коэффициент фаз полета» для определения походки агента. Фаза полета — это фаза, в которой обе стопы не соприкасаются с землей в течение одного полного цикла ходьбы. В соответствии с соотношением фаз полета скорость движения, контролируемая AI-CPG, разделена на три периода черной пунктирной линией на 3a и 3d.

Область слева представляет собой устойчивую походку без фазы полета. Область в центре представляет собой переходную фазу походки, при которой фаза полета колеблется. Область справа представляет собой стабильную беговую походку, при которой соотношение фаз полета больше нуля и постоянно увеличивается с увеличением скорости. Результат подтверждает, что AI-CPG позволяет нейронной сети изучать различные походки, включая переходы от ходьбы к бегу.

На основании сравнения трех вышеперечисленных алгоритмов можно сделать вывод, что AI-CPG сочетает в себе преимущества имитационного обучения и DRL. Посредством формирования функции вознаграждения DRL помогает роботу научиться сохранять баланс и оптимизировать энергоэффективность. Походка, подобная человеческой, полученная на основе баз данных, позволяет AI-CPG лучше противостоять внешним воздействиям. Кроме того, механизм упреждающего управления в части CPG AI-CPG позволяет ему последовательно обрабатывать широкий диапазон скоростей движения и различных походок.

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Изображение №4

Регулируя значение частоты (f) на контрольной точке, ученые добились плавного перехода от ходьбы к бегу. Связь между f и временем t составляет f(t) = 0.7 + 0.023t. Как показано на 4a, увеличение f приводит к постепенному уплотнению входного синусоидального сигнала в AI-CPG, что приводит к регулируемой динамической походке. Между тем, на 4b показаны изменения походки на разных стадиях. Когда и t, и f малы, робот движется медленно шагающей походкой. Соотношение фаз полета (Fr) и диаграмма походки на 4d подтверждают это наблюдение. По мере увеличения t и f скорость робота также увеличивалась, а соотношение фаз полета постепенно увеличивалось и колебалось, что указывает на переходную походку робота. Когда частота превышает 1.14 Гц, робот переходит к устойчивому бегу с дальнейшим увеличением t и f (4e).

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Изображение №5

Далее ученые приступили к тестированию системы, использующей алгоритмы PPO, AI-CPG или AMP, в условиях неровной поверхности (перепады высоты в 10 см). На 5a показаны изменения траектории движения робота по мере увеличения обучающих итераций при разных целевых скоростях. Результаты показывают, что PPO и AI-CPG успешно управляли движением робота на неровной местности, тогда как AMP не смог справиться с этой задачей.

На ранних этапах обучения и PPO, и AI-CPG имели неупорядоченный вектор скорости (обозначенный темной стрелкой на изображении выше), который группировался вокруг начала координат, что делало агента неспособным эффективно двигаться. По мере увеличения количества обучающих итераций вектор скорости постепенно выравнивался с положительной осью X и смещался вправо, указывая на то, что агент научился двигаться в желаемом направлении. И наоборот, траектория и векторы скорости AMP всегда были неорганизованными и случайными.

На изображении 5b показано сравнение экспериментальных результатов PPO и AI-CPG. Сравнение показало, что AI-CPG превосходит PPO с точки зрения индекса симметрии, индекса баланса и CoT при двух разных скоростях движения. Важно отметить, что в случае AI-CPG используется одна и та же нейронная сеть как для ходьбы, так и для бега, тогда как для PPO требуется нейронная сеть для ходьбы и еще одна для бега. Кроме того, стандартное отклонение результатов AI-CPG было меньше, что указывает на более стабильный процесс обучения.

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Изображение№6

Наконец, был протестирован переход от ходьбы к бегу на неровной местности, что является достаточно сложной задачей. AI-CPG, как и в случае с ровной поверхностью, успешно справился с данной задачей (график выше).

Принцип работы обучающей системы AI-CPG.

Для более детального ознакомления с нюансами исследования рекомендую заглянуть в доклад ученых.

Эпилог


В рассмотренном нами сегодня труде ученые описали новую систему управления движений человекоподобных роботов (AI-CPG), основанную на имитационном обучении и усиленных рефлекторных сетях.

На первый взгляд ходьба или даже бег кажутся весьма простыми для реализации процессами. Однако эта простота обусловлена биологическими процессами, которые позволяют нам в реальном времени адаптироваться к динамической окружающей среде. Но для двуногих роботов подобная адаптируемость является практически непосильной задачей. Суть в том, что роботу необходимо иметь базу данных потенциальных изменений рабочей среды, к которой он может обратиться в случае реализации одного из изменений. Нам это не нужно, ведь процесс адаптации происходит сам по себе, т. е. без обязательной привязки к предыдущему опыту.

Авторы исследования решили использовать глубокое обучение с подкреплением (DRL от deep reinforcement learning), которое расширяет традиционное обучение с подкреплением, используя глубокие нейронные сети для решения более сложных задач и обучения непосредственно на основе необработанных сенсорных данных. Однако DRL обладает весьма существенным недостатком, а именно огромными вычислительными затратами.

Другой подход, называемый имитационным обучением, использует данные о движения реальных людей. Этот метод отлично справляется в ситуациях стабильных условий (например, полностью ровная поверхность), но становится малоэффективным, когда условия меняются (например, перепады высоты на поверхности).

Ученые смогли преодолеть ограничения вышеописанных методов, объединив их в единое целое. В результате робот был обучен не только ходить и бегать, но и подстраиваться под динамически меняющуюся среду и плавно переходить от ходьбы к бегу.

Авторы разработки уверены, что их труд является значимым шагом к реализации генеративного искусственного интеллекта для управления роботами с потенциальным применением в различных отраслях.

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Источник

  • 07.09.23 16:24 CherryTeam

    Cherry Team atlyginimų skaičiavimo programa yra labai naudingas įrankis įmonėms, kai reikia efektyviai valdyti ir skaičiuoti darbuotojų atlyginimus. Ši programinė įranga, turinti išsamias funkcijas ir patogią naudotojo sąsają, suteikia daug privalumų, kurie padeda supaprastinti darbo užmokesčio skaičiavimo procesus ir pagerinti finansų valdymą. Štai keletas pagrindinių priežasčių, kodėl Cherry Team atlyginimų skaičiavimo programa yra naudinga įmonėms: Automatizuoti ir tikslūs skaičiavimai: Atlyginimų skaičiavimai rankiniu būdu gali būti klaidingi ir reikalauti daug laiko. Programinė įranga Cherry Team automatizuoja visą atlyginimų skaičiavimo procesą, todėl nebereikia atlikti skaičiavimų rankiniu būdu ir sumažėja klaidų rizika. Tiksliai apskaičiuodama atlyginimus, įskaitant tokius veiksnius, kaip pagrindinis atlyginimas, viršvalandžiai, premijos, išskaitos ir mokesčiai, programa užtikrina tikslius ir be klaidų darbo užmokesčio skaičiavimo rezultatus. Sutaupoma laiko ir išlaidų: Darbo užmokesčio valdymas gali būti daug darbo jėgos reikalaujanti užduotis, reikalaujanti daug laiko ir išteklių. Programa Cherry Team supaprastina ir pagreitina darbo užmokesčio skaičiavimo procesą, nes automatizuoja skaičiavimus, generuoja darbo užmokesčio žiniaraščius ir tvarko išskaičiuojamus mokesčius. Šis automatizavimas padeda įmonėms sutaupyti daug laiko ir pastangų, todėl žmogiškųjų išteklių ir finansų komandos gali sutelkti dėmesį į strategiškai svarbesnę veiklą. Be to, racionalizuodamos darbo užmokesčio operacijas, įmonės gali sumažinti administracines išlaidas, susijusias su rankiniu darbo užmokesčio tvarkymu. Mokesčių ir darbo teisės aktų laikymasis: Įmonėms labai svarbu laikytis mokesčių ir darbo teisės aktų, kad išvengtų baudų ir teisinių problemų. Programinė įranga Cherry Team seka besikeičiančius mokesčių įstatymus ir darbo reglamentus, užtikrindama tikslius skaičiavimus ir teisinių reikalavimų laikymąsi. Programa gali dirbti su sudėtingais mokesčių scenarijais, pavyzdžiui, keliomis mokesčių grupėmis ir įvairių rūšių atskaitymais, todėl užtikrina atitiktį reikalavimams ir kartu sumažina klaidų riziką. Ataskaitų rengimas ir analizė: Programa Cherry Team siūlo patikimas ataskaitų teikimo ir analizės galimybes, suteikiančias įmonėms vertingų įžvalgų apie darbo užmokesčio duomenis. Ji gali generuoti ataskaitas apie įvairius aspektus, pavyzdžiui, darbo užmokesčio paskirstymą, išskaičiuojamus mokesčius ir darbo sąnaudas. Šios ataskaitos leidžia įmonėms analizuoti darbo užmokesčio tendencijas, nustatyti tobulintinas sritis ir priimti pagrįstus finansinius sprendimus. Pasinaudodamos duomenimis pagrįstomis įžvalgomis, įmonės gali optimizuoti savo darbo užmokesčio strategijas ir veiksmingai kontroliuoti išlaidas. Integracija su kitomis sistemomis: Cherry Team programinė įranga dažnai sklandžiai integruojama su kitomis personalo ir apskaitos sistemomis. Tokia integracija leidžia automatiškai perkelti atitinkamus duomenis, pavyzdžiui, informaciją apie darbuotojus ir finansinius įrašus, todėl nebereikia dubliuoti duomenų. Supaprastintas duomenų srautas tarp sistemų padidina bendrą efektyvumą ir sumažina duomenų klaidų ar neatitikimų riziką. Cherry Team atlyginimų apskaičiavimo programa įmonėms teikia didelę naudą - automatiniai ir tikslūs skaičiavimai, laiko ir sąnaudų taupymas, atitiktis mokesčių ir darbo teisės aktų reikalavimams, ataskaitų teikimo ir analizės galimybės bei integracija su kitomis sistemomis. Naudodamos šią programinę įrangą įmonės gali supaprastinti darbo užmokesčio skaičiavimo procesus, užtikrinti tikslumą ir atitiktį reikalavimams, padidinti darbuotojų pasitenkinimą ir gauti vertingų įžvalgų apie savo finansinius duomenis. Programa Cherry Team pasirodo esanti nepakeičiamas įrankis įmonėms, siekiančioms efektyviai ir veiksmingai valdyti darbo užmokestį. https://cherryteam.lt/lt/

  • 08.10.23 01:30 davec8080

    The "Shibarium for this confirmed rug pull is a BEP-20 project not related at all to Shibarium, SHIB, BONE or LEASH. The Plot Thickens. Someone posted the actual transactions!!!! https://bscscan.com/tx/0xa846ea0367c89c3f0bbfcc221cceea4c90d8f56ead2eb479d4cee41c75e02c97 It seems the article is true!!!! And it's also FUD. Let me explain. Check this link: https://bscscan.com/token/0x5a752c9fe3520522ea88f37a41c3ddd97c022c2f So there really is a "Shibarium" token. And somebody did a rug pull with it. CONFIRMED. But the "Shibarium" token for this confirmed rug pull is a BEP-20 project not related at all to Shibarium, SHIB, BONE or LEASH.

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  • 23.09.24 18:56 matthewshimself

    At first, I was admittedly skeptical about Worldcoin (ref: https://worldcoin.org/blog/worldcoin/this-is-worldcoin-video-explainer-series), particularly around the use of biometric data and the WLD token as a reward mechanism for it. However, after following the project closer, I’ve come to appreciate the broader vision and see the value in the underlying tech behind it. The concept of Proof of Personhood (ref: https://worldcoin.org/blog/worldcoin/proof-of-personhood-what-it-is-why-its-needed) has definitely caught my attention, and does seem like a crucial step towards tackling growing issues like bots, deepfakes, and identity fraud. Sam Altman’s vision is nothing short of ambitious, but I do think he & Alex Blania have the chops to realize it as mainstay in the global economy.

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  • 02.10.24 22:27 Emily Hunter

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  • 31.10.24 00:13 ytre89

    Can those who have fallen victim to fraud get their money back? Yes, you might be able to get back what was taken from you if you fell prey to a fraud from an unregulated investing platform or any other scam, but only if you report it to the relevant authorities. With the right plan and supporting documentation, you can get back what you've lost. Most likely, the individuals in control of these unregulated platforms would attempt to convince you that what happened to your money was a sad accident when, in fact, it was a highly skilled heist. You should be aware that there are resources out there to help you if you or someone you know has experienced one of these circumstances. Do a search using (deftrecoup (.) c o m). Do not let the perpetrators of this hoaxes get away with ruining you mentally and financially.

  • 02.11.24 14:44 diannamendoza732

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  • 12.11.24 00:50 TERESA

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  • 17.11.24 09:31 Vivianlocke223

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    Being a parent is great until your toddler figures out how to use your devices. One afternoon, I left my phone unattended for just a few minutes rookie mistake of the century. I thought I’d take a quick break, but little did I know that my curious little genius was about to embark on a digital adventure. By the time I came back, I was greeted by two shocking revelations: my toddler had somehow managed to buy a $5 dinosaur toy online and, even more alarmingly, had locked me out of my cryptocurrency wallet holding a hefty $75,000. Yes, you heard that right a dinosaur toy was the least of my worries! At first, I laughed it off. I mean, what toddler doesn’t have a penchant for expensive toys? But then reality set in. I stared at my phone in disbelief, desperately trying to guess whatever random string of gibberish my toddler had typed as a new password. Was it “dinosaur”? Or perhaps “sippy cup”? I felt like I was in a bizarre game of Password Gone Wrong. Every attempt led to failure, and soon the laughter faded, replaced by sheer panic. I was in way over my head, and my heart raced as the countdown of time ticked away. That’s when I decided to take action and turned to Digital Tech Guard Recovery, hoping they could solve the mystery that was my toddler’s handiwork. I explained my predicament, half-expecting them to chuckle at my misfortune, but they were incredibly professional and empathetic. Their confidence put me at ease, and I knew I was in good hands. Contact With WhatsApp: +1 (443) 859 - 2886  Email digital tech guard . com  Telegram: digital tech guard recovery . com  website link :: https : // digital tech guard . com Their team took on the challenge like pros, employing their advanced techniques to unlock my wallet with a level of skill I can only describe as magical. As I paced around, anxiously waiting for updates, I imagined my toddler inadvertently locking away my life savings forever. But lo and behold, it didn’t take long for Digital Tech Guard Recovery to work their magic. Not only did they recover the $75,000, but they also gave me invaluable tips on securing my wallet better like not leaving it accessible to tiny fingers! Who knew parenting could lead to such dramatic situations? Crisis averted, and I learned my lesson: always keep my devices out of reach of little explorers. If you ever find yourself in a similar predicament whether it’s tech-savvy toddlers or other digital disasters don’t hesitate to reach out to Digital Tech Guard Recovery. They saved my funds and my sanity, proving that no challenge is too great, even when it involves a toddler’s mischievous fingers!

  • 21.11.24 08:02 Emily Hunter

    If I hadn't found a review online and filed a complaint via email to support@deftrecoup. com , the people behind this unregulated scheme would have gotten away with leaving me in financial ruins. It was truly the most difficult period of my life.

  • 22.11.24 04:41 [email protected]

    I never could have imagined the nightmare of losing access to my digital wallet. All of my cryptocurrency holdings were abruptly imprisoned, inaccessible, and appeared to be lost forever following a catastrophic hardware breakdown. Years of meticulous investment and careful saving were reduced to nothing more than strings of code that I could no longer control, and I could feel the dread and sorrow that swept through me at that very instant. Thankfully, during my worst moment, I came into (TRUST GEEKS HACK EXPERT), a professional service devoted to recovering lost or inaccessible digital data. With optimism, I went out to their team of skilled technologists, laying bare the full nature of my issue. What followed was a laborious, multi-step process that required an almost surgical level of digital forensics and Bitcoin skill. In order to create a thorough profile of my wallet's contents and activities, the (TRUST GEEKS HACK EXPERT) team first thoroughly examined the transaction history and metadata connected to it. Next, they implemented a series of advanced recovery techniques, using cutting-edge software tools to bypass the access barriers that had left me locked out. The entire process was shrouded in secrecy and discretion, with the (TRUST GEEKS HACK EXPERT) team working tirelessly to protect the confidentiality of my sensitive financial information. After what felt like an eternity of nervous anticipation, the day finally arrived when I received the triumphant notification – my wallet had been successfully restored, and all of my precious digital assets had been returned to my control. The sense of relief was indescribable, as I could finally breathe easy knowing that the fruits of my financial discipline had been safeguarded. While the experience of losing access to my wallet was undoubtedly traumatic, (TRUST GEEKS HACK EXPERT) intervention allowed me to emerge from the ordeal with my cryptocurrency holdings intact, and a renewed appreciation for the importance of proactive digital asset management. You can contact Them through EMAIL: [email protected] - TELEGRAM: TRUSTGEEKSHACKEXPERT

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  • 22.11.24 23:43 teresaborja

    all thanks to Tech Cyber Force Recovery expert assistance. As a novice in cryptocurrency, I had been carefully accumulating a modest amount of Bitcoin, meticulously safeguarding my digital wallet and private keys. However, as the adage goes, the best-laid plans can often go awry, and that's precisely what happened to me. Due to a series of technical mishaps and human errors, I found myself locked out of my Bitcoin wallet, unable to access the fruits of my digital labors. Panic set in as I frantically searched for a solution, scouring the internet for any glimmer of hope. That's when I stumbled upon the Tech Cyber Force Recovery team, a group of seasoned cryptocurrency specialists who had built a reputation for their ability to recover lost or inaccessible digital assets. Skeptical at first, I reached out, desperate for a miracle. To my utter amazement, the Tech Cyber Force Recovery experts quickly assessed my situation and devised a meticulous plan of attack. Through their deep technical knowledge, unwavering determination, and a keen eye for detail, they were able to navigate the complex labyrinth of blockchain technology, ultimately recovering my entire Bitcoin portfolio. What had once seemed like a hopeless endeavor was now a reality, and I found myself once again in possession of my digital wealth, all thanks to the incredible efforts of the Tech Cyber Force Recovery team. This experience has not only restored my faith in the cryptocurrency ecosystem. Still, it has also instilled in me a profound appreciation for the critical role that expert recovery services can play in safeguarding one's digital assets.   ENAIL < Tech cybers force recovery @ cyber services. com >   WEBSITE < ht tps : // tech cyber force recovery. info  >   TEXT < +1. 561. 726. 3697 >

  • 24.11.24 02:21 [email protected]

    I never could have imagined the nightmare of losing access to my digital wallet. All of my cryptocurrency holdings were abruptly imprisoned, inaccessible, and appeared to be lost forever following a catastrophic hardware breakdown. Years of meticulous investment and careful saving were reduced to nothing more than strings of code that I could no longer control, and I could feel the dread and sorrow that swept through me at that very instant. Thankfully, during my worst moment, I came into (TRUST GEEKS HACK EXPERT), a professional service devoted to recovering lost or inaccessible digital data. With optimism, I went out to their team of skilled technologists, laying bare the full nature of my issue. What followed was a laborious, multi-step process that required an almost surgical level of digital forensics and Bitcoin skill. In order to create a thorough profile of my wallet's contents and activities, the (TRUST GEEKS HACK EXPERT) team first thoroughly examined the transaction history and metadata connected to it. Next, they implemented a series of advanced recovery techniques, using cutting-edge software tools to bypass the access barriers that had left me locked out. The entire process was shrouded in secrecy and discretion, with the (TRUST GEEKS HACK EXPERT) team working tirelessly to protect the confidentiality of my sensitive financial information. After what felt like an eternity of nervous anticipation, the day finally arrived when I received the triumphant notification – my wallet had been successfully restored, and all of my precious digital assets had been returned to my control. The sense of relief was indescribable, as I could finally breathe easy knowing that the fruits of my financial discipline had been safeguarded. While the experience of losing access to my wallet was undoubtedly traumatic, (TRUST GEEKS HACK EXPERT) intervention allowed me to emerge from the ordeal with my cryptocurrency holdings intact, and a renewed appreciation for the importance of proactive digital asset management. You can contact Them through EMAIL: [email protected] - TELEGRAM: TRUSTGEEKSHACKEXPERT

  • 25.11.24 02:19 briankennedy

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  • 26.11.24 21:59 [email protected]

    In a world brimming with enticing investment opportunities, it is crucial to tread carefully. The rise of digital currencies has attracted many eager investors, but along with this excitement lurk deceitful characters ready to exploit the unsuspecting. I learned this lesson the hard way, and I want to share my story in the hopes that it can save someone from making the same mistakes I did. It all began innocently enough when I came across an engaging individual on Facebook. Lured in by promises of high returns in the cryptocurrency market, I felt the electric thrill of potential wealth coursing through me. Initial investments returned some profits, and that exhilarating taste of success fueled my ambition. Encouraged by a meager withdrawal, I decided to commit even more funds. This was the moment I let my guard down, blinded by greed. As time went on, the red flags started to multiply. The moment I tried to withdraw my earnings, a cascade of unreasonable fees appeared like a thick mist, obscuring the truth. “Just a little more,” they said, “Just until the next phase.” I watched my hard-earned money slip through my fingers as I scraped together every last cent to pay those relentless fees. My trust had become my downfall. In the end, I lost not just a significant amount of cash, but my peace of mind about $1.1 million vanished into the abyss of false promises and hollow guarantees. But despair birthed hope. After a cascade of letdowns, I enlisted the help of KAY-NINE CYBER SERVICES, a team that specializes in reclaiming lost funds from scams. Amazingly, they worked tirelessly to piece together what had been ripped away, providing me with honest guidance when I felt utterly defeated. Their expertise in navigating the treacherous waters of crypto recovery was a lifeline I desperately needed. To anyone reading this, please let my story serve as a warning. High returns often come wrapped in the guise of deception. Protect your investments, scrutinize every opportunity, and trust your instincts. Remember, the allure of quick riches can lead you straight to heartbreak, but with cautious determination and support, it is possible to begin healing from such devastating loss. Stay informed, stay vigilant, and may you choose your investment paths wisely. Email: kaynine @ cyberservices . com

  • 26.11.24 23:12 rickrobinson8

    FAST SOLUTION FOR CYPTOCURRENCY RECOVERY SPARTAN TECH GROUP RETRIEVAL

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  • 27.11.24 00:39 [email protected]

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  • 27.11.24 09:10 Michal Novotny

    The biggest issue with cryptocurrency is that it is unregulated, wh ich is why different people can come up with different fake stories all the time, and it is unfortunate that platforms like Facebook and others only care about the money they make from them through ads. I saw an ad on Facebook for Cointiger and fell into the scam, losing over $30,000. I reported it to Facebook, but they did nothing until I discovered deftrecoup . c o m from a crypto community; they retrieved approximately 95% of the total amount I lost.

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