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Explore how driving simulator setups transform the landscape of autonomous vehicles. They detect situational awareness, enabling seamless human-robot interactions. #drivingsimulator #humanrobots #fridaysforfuture #technology #techtrends #robotics #futuretechnology #ONPASSIVE #TheFutureOfInternet

ONPASSIVE

48,354 subscribers

14,397 views • 3 years ago •via X (Twitter)

Science & Technology #drivingsimulator #humanrobots #fridaysforfuture #technology #techtrends #robotics #futuretechnology #ONPASSIVE

Anya Rossi• Live Now

Private livecam show

9 Comments

Dharmendra Malviaa3 years ago

#ONPASSIVE in the take a seat and fasten your seatbelt as we accelerate towards an unforgettable advantages #drivingsimulator #humanrobots #fridaysforfuture #technology #techtrends #robotics #futuretechnology #ONPASSIVE #TheFutureOfInternet

Mr. Ranjan Maity3 years ago

Technology is the best solutions of future life. #ONPASSIVE #TheFutureOfInternet #drivingsimulator #humanrobots #robotics #technoligy #techtrands #futuretechnology

ONPASSIVE3 years ago

Thank you for your response! Have a wonderful day.

B.S REDDY3 years ago

Onpossive bright future

rick segal3 years ago

ONPASSIVE will change the face of the internet

Dheeraj $BEYOND3 years ago

#ONPASSIVE #TheFutureOfInternet

RASHEDUL ISLAM3 years ago

#ONPASSIVE #TheFutureOfInternet

SHIV VARDAN3 years ago

#drivingsimulator #humanrobots #fridaysforfuture #technology #techtrends #robotics #futuretechnology #ONPASSIVE #TheFutureOfInternet

A I power3 years ago

Very nice 👍

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Autonomous dodging off-roads without any human presence. This is a demonstration of pushing the limits of what is possible in off-road #autonomousdriving. In this demo, we performed autonomous dodging without any human presence in our autonomous vehicle, that too off-roads. In this mode, collision avoidance against aggressive-stochastic-adversarial traffic is the sole responsibility of #AutonomousVehicles. Our autonomous vehicle, Deep Xplorer, was tasked with navigating an off-road trail, avoiding both the static and dynamic obstacles. Furthermore, team members on bikes introduced random cross-traffic interactions by cutting its path at random, creating highly adversarial and stochastic traffic scenarios. To increase the stakes even further, I personally drove our other autonomous vehicle (Xplorer), cutting the path of Deep Xplorer at random --leaving the task of negotiation, motion planning, and decision making, to ensure collision avoidance and safety of the vehicles and the environment as a sole responsibility of our autonomous driving stack in Deep Xplorer. This is the first time in the history of Swaayatt Robots that we performed autonomous dodging without human presence in our autonomous vehicle, pushing the limits of what is possible in autonomous driving landscape. #reinforcementlearning #deeplearning

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Human, chill. I will take care of your car! 🚗 Stanley Robotics engineers precise mobile robots capable of moving, parking, and delivering vehicles without human intervention. Robots need to know their environment, so they first create virtual representations of physical sites. All of that to enable real-time management, optimization, and visualization. Fleet manager technology dispatches robot fleets based on calculated trajectories, battery status, and task requirements. On a side note, they are an API-first company and have developed a proprietary API to facilitate communication between their systems and customer existing management software. Less parking lot accidents? 🤕 ~~ ♻ Join the weekly robotics newsletter, and never miss any news →

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18,829 views • 1 month ago

1. This is Elon Musk presenting what the future of cars, referred to as Cybercabs, will look like in less than two years. He presented this at the “We, Robot” event, where he introduced autonomous vehicles and robots. The unfortunate reality is that, on the same day when the world’s richest man, known for his work in technology, was presenting this, Zimbabwe’s ambassador to Europe and the Americas, and President Emmerson Mnangagwa’s advisor, made the most embarrassing, outrageous, and foolish remarks about Elon Musk and his companies. See Twitter 2 below. He called this anti-Christ and the work of the devil. Africa is cursed for sure with such idiots in power, how can it develop? These vehicles, like Tesla’s Model Y and Cybercab, aim to reduce travel costs, maximise vehicle usage, and enhance convenience. Musk compared autonomous transport to past innovations like elevators and said that autonomous cars are becoming safer and more efficient than human driving. Musk envisions cities with fewer parking lots, replaced by parks, thanks to reduced car ownership needs. Autonomous vehicles are expected to roll out fully by 2026. Robovans, designed for high-density travel and goods transport, were also introduced. The event showcased these technologies, promising an exciting future of autonomy and robotics.

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46,743 views • 1 year ago

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Today, our young Engineers learned how to solder connections to DC motors and use the L298N motor driver to control the speed and direction of the motors. These motors will form part of the circuit for their self-balancing robot, where two wheels must constantly adjust their movement to keep the robot upright and stable. But this lesson goes far beyond building one robot. The same fundamental principles of motor control, feedback, and stabilization are applied in bipedal robots, smart drones, camera gimbal stabilization systems, and many other robotic systems. As they progress, the children will also learn how to use PID (Proportional–Integral-Derivative) control, an algorithm that allows a system to detect changes, calculate the necessary correction, and continuously adjust its response to maintain stability. So, they are not simply learning how to connect and drive a motor. They are beginning to understand some of the core engineering principles behind modern robotics and autonomous systems.

Alex Onyia

35,637 views • 1 month ago

A few self-driving taxis in San Francisco just demonstrated the real problem with autonomy. They were too rational. For a brief moment, several robotaxis aligned at an intersection and created a perfectly polite deadlock. No aggression. No improvisation. No human-style “you go, I’ll go.” Just algorithms waiting for clarity. And that is exactly why this moment matters. What interests me is that this was not a failure of sensing. The vehicles could see. The problem was social judgment. Because cities are not just physical systems. They are negotiation systems. They run on: → tiny signals → hesitation → assertiveness → eye contact → imperfect timing That is where autonomy gets much harder than people think. We are not only teaching machines how to detect objects and follow lanes. We are asking them to operate inside messy human environments where the right move is not always the most logical one. To me, that is the deeper lesson. The next frontier in self-driving is not just better perception. It is better judgment under uncertainty. And that is a much more difficult problem. What do you think matters more for autonomous vehicles now: seeing the road better, or learning how to navigate human ambiguity? #AI #AutonomousVehicles #SelfDrivingCars #FutureOfMobility #Innovation #Technology #SmartCities #MachineLearning #FutureOfWork

A few self-driving taxis in San Francisco just demonstrated the real problem with autonomy. They were too rational. For a brief moment, several robotaxis aligned at an intersection and created a perfectly polite deadlock. No aggression. No improvisation. No human-style “you go, I’ll go.” Just algorithms waiting for clarity. And that is exactly why this moment matters. What interests me is that this was not a failure of sensing. The vehicles could see. The problem was social judgment. Because cities are not just physical systems. They are negotiation systems. They run on: → tiny signals → hesitation → assertiveness → eye contact → imperfect timing That is where autonomy gets much harder than people think. We are not only teaching machines how to detect objects and follow lanes. We are asking them to operate inside messy human environments where the right move is not always the most logical one. To me, that is the deeper lesson. The next frontier in self-driving is not just better perception. It is better judgment under uncertainty. And that is a much more difficult problem. What do you think matters more for autonomous vehicles now: seeing the road better, or learning how to navigate human ambiguity? #AI #AutonomousVehicles #SelfDrivingCars #FutureOfMobility #Innovation #Technology #SmartCities #MachineLearning #FutureOfWork

Pascal Bornet

81,430 views • 5 months ago

China’s Robot-Served Kiosks Showcase Next Generation of Automated Technology! In bustling urban centers across China, the future of everyday service is already here. Among the most striking examples are robot-served kiosks where autonomous machines prepare and serve drinks and snacks, drawing attention from locals and international visitors alike. Travelers exploring China’s cities have encountered these cutting-edge kiosks in public spaces and transit hubs. The machines, operating with little to no human assistance, handle everything from taking orders to preparing and dispensing beverages, showcasing the rapid pace of automation in everyday life. The increasing presence of robotic kiosks reflects China’s broader push into smart technology and artificial intelligence. These automated stations use advanced robotics, computer vision, and AI to deliver efficient, consistent service. Visitors often express surprise at the speed and sophistication of the machines, underscoring how far automated retail has come from simple vending devices to fully interactive service points. These robot-served kiosks offer a tangible glimpse of how robotics could reshape the future of service industries, merging utility with novelty and giving a preview of broader automation trends shaping economies around the world.

China’s Robot-Served Kiosks Showcase Next Generation of Automated Technology! In bustling urban centers across China, the future of everyday service is already here. Among the most striking examples are robot-served kiosks where autonomous machines prepare and serve drinks and snacks, drawing attention from locals and international visitors alike. Travelers exploring China’s cities have encountered these cutting-edge kiosks in public spaces and transit hubs. The machines, operating with little to no human assistance, handle everything from taking orders to preparing and dispensing beverages, showcasing the rapid pace of automation in everyday life. The increasing presence of robotic kiosks reflects China’s broader push into smart technology and artificial intelligence. These automated stations use advanced robotics, computer vision, and AI to deliver efficient, consistent service. Visitors often express surprise at the speed and sophistication of the machines, underscoring how far automated retail has come from simple vending devices to fully interactive service points. These robot-served kiosks offer a tangible glimpse of how robotics could reshape the future of service industries, merging utility with novelty and giving a preview of broader automation trends shaping economies around the world.

The AI Robot Guy on X

35,740 views • 7 months ago

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Wimar.X

39,246 views • 1 month ago

NEWS: Waymo has released a new blog post detailing their AI strategy and how it’s allowing them to bring service to more riders faster. "Achieving demonstrably safe AI — where safety is proven, not just promised — requires a holistic approach. Beyond a smart and capable Driver, you also need a closed-loop, realistic Simulator to train and rigorously test the Driver in a myriad of challenging situations, and a sharp Critic to evaluate the Driver's performance and identify areas for improvement." Waymo says that autonomous driving isn’t just a matter of building a “smart driver,” but rather creating a full AI ecosystem centered on safety from the ground up. At the core is the Waymo Foundation Model, a unified world-model that powers all major components of Waymo’s autonomous stack (Driver, Simulator, Critic). "By using a “Think Fast/Think Slow” architecture (combining rapid sensor-fusion with deep semantic reasoning), this system enables the car to detect complex and rare road scenarios (e.g. a burning vehicle ahead), reason about them, and choose safe behavior. Waymo trains large “Teacher” AI-models for driving, simulation, and evaluation, then distills them into smaller, efficient “Student” models suitable for real-world deployment, while keeping safety validation tightly integrated. The result is a continuous “flywheel” of learning: driving data (real and simulated) generate feedback, which leads to refinements, more simulation, more data, and only when safety checks pass is new code deployed. Having already exceeded 100 million fully autonomous miles, Waymo reports a more than ten-fold reduction in severe-injury crashes compared to human drivers." Full blog post:

NEWS: Waymo has released a new blog post detailing their AI strategy and how it’s allowing them to bring service to more riders faster. "Achieving demonstrably safe AI — where safety is proven, not just promised — requires a holistic approach. Beyond a smart and capable Driver, you also need a closed-loop, realistic Simulator to train and rigorously test the Driver in a myriad of challenging situations, and a sharp Critic to evaluate the Driver's performance and identify areas for improvement." Waymo says that autonomous driving isn’t just a matter of building a “smart driver,” but rather creating a full AI ecosystem centered on safety from the ground up. At the core is the Waymo Foundation Model, a unified world-model that powers all major components of Waymo’s autonomous stack (Driver, Simulator, Critic). "By using a “Think Fast/Think Slow” architecture (combining rapid sensor-fusion with deep semantic reasoning), this system enables the car to detect complex and rare road scenarios (e.g. a burning vehicle ahead), reason about them, and choose safe behavior. Waymo trains large “Teacher” AI-models for driving, simulation, and evaluation, then distills them into smaller, efficient “Student” models suitable for real-world deployment, while keeping safety validation tightly integrated. The result is a continuous “flywheel” of learning: driving data (real and simulated) generate feedback, which leads to refinements, more simulation, more data, and only when safety checks pass is new code deployed. Having already exceeded 100 million fully autonomous miles, Waymo reports a more than ten-fold reduction in severe-injury crashes compared to human drivers." Full blog post:

Sawyer Merritt

83,635 views • 9 months ago