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QACE Dynamics | Autonomous Navigation Preview Today we are showing autonomous navigation running live inside QACE, the robot plans its route on the map, then adjusts in real time as lidar and vision pick up new obstacles. All of this runs through a single navigation block inside QACE, ready...

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Qace Dynamics

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12,654 просмотров • 8 месяцев назад •via X (Twitter)

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QACE Dynamics | Inside the Making of the Robotics Lab A first look at the core setup behind QACE. Three Jetson Orin NX based QARC units, an Intel mini system, and a firewall are now active as part of the infrastructure powering the decentralised robotics lab. The QACE dApp is already connected to these systems, running live simulations and processing autonomy workloads directly on them. These NVIDIA-powered edge units serve as the backbone for real-time AI inference, multi-sensor processing, and distributed control inside the QACE ecosystem. Very soon, AGVs, robotic arms, and vision systems will be connected to this network as we move toward a live, fully integrated setup. Stay tuned for the next video where we show the robots running on this infrastructure. #QACE #RoboticsAI #EdgeAI #QARC #RoboticsLab #Qognix
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QACE Dynamics | Inside the Making of the Robotics Lab A first look at the core setup behind QACE. Three Jetson Orin NX based QARC units, an Intel mini system, and a firewall are now active as part of the infrastructure powering the decentralised robotics lab. The QACE dApp is already connected to these systems, running live simulations and processing autonomy workloads directly on them. These NVIDIA-powered edge units serve as the backbone for real-time AI inference, multi-sensor processing, and distributed control inside the QACE ecosystem. Very soon, AGVs, robotic arms, and vision systems will be connected to this network as we move toward a live, fully integrated setup. Stay tuned for the next video where we show the robots running on this infrastructure. #QACE #RoboticsAI #EdgeAI #QARC #RoboticsLab #Qognix

Qace Dynamics

10,191 просмотров • 8 месяцев назад

QACE Dynamics | VIBR Tech Preview 1 Introducing VIBR, the vision intelligence block that brings real perception to robots. In this first preview, we take a simple vacuum bot, connect it through the QACE dApp, and instantly give it object detection and smarter behavior without touching code. QACE makes it easy by automating the usual workflow: what once took hours of coding in Python and assembling AI models is now reduced to a few clicks. Complex AI vision blocks are created, configured, and ready-to-deploy scripts are generated automatically. Robots can now see, decide, and act in real time. Up next: we will show how these rules and actions evolve into full autonomy modules, with the Qognix Tech Preview dropping this week.
1:59

QACE Dynamics | VIBR Tech Preview 1 Introducing VIBR, the vision intelligence block that brings real perception to robots. In this first preview, we take a simple vacuum bot, connect it through the QACE dApp, and instantly give it object detection and smarter behavior without touching code. QACE makes it easy by automating the usual workflow: what once took hours of coding in Python and assembling AI models is now reduced to a few clicks. Complex AI vision blocks are created, configured, and ready-to-deploy scripts are generated automatically. Robots can now see, decide, and act in real time. Up next: we will show how these rules and actions evolve into full autonomy modules, with the Qognix Tech Preview dropping this week.

Qace Dynamics

29,448 просмотров • 10 месяцев назад

Qace Dynamics | End to End Autonomy Today we show the full workflow of how QACE brings autonomy to robots. Inside the dApp you can define inputs, outputs, and rules, then add modular AI blocks that map directly into reusable ROS2 nodes and DDS communication interfaces. The system interprets natural instructions through the NLP core, compiles them into abstract syntax trees, builds the directed execution graph, and generates the scripts automatically. The workflow is validated back in Gazebo where detections, temporal operators, and event triggers are applied in real time against the simulation runtime. From there the same pipeline can be containerized and deployed on QARC or directly on the robot with no additional coding or manual integration. This is how we replace fragmented pipelines and redundant middleware with one unified framework where high level intent is compiled into deterministic autonomy graphs.
2:07

Qace Dynamics | End to End Autonomy Today we show the full workflow of how QACE brings autonomy to robots. Inside the dApp you can define inputs, outputs, and rules, then add modular AI blocks that map directly into reusable ROS2 nodes and DDS communication interfaces. The system interprets natural instructions through the NLP core, compiles them into abstract syntax trees, builds the directed execution graph, and generates the scripts automatically. The workflow is validated back in Gazebo where detections, temporal operators, and event triggers are applied in real time against the simulation runtime. From there the same pipeline can be containerized and deployed on QARC or directly on the robot with no additional coding or manual integration. This is how we replace fragmented pipelines and redundant middleware with one unified framework where high level intent is compiled into deterministic autonomy graphs.

Qace Dynamics

53,180 просмотров • 10 месяцев назад

QACE Dynamics | Qognix Rule Engine Qognix is built on a library of modular AI blocks that can be reused across many applications. Each block has NLP built in, so it understands natural instructions and converts them into intents, rules, and actions. The blocks are organized as tiles for specific domains such as surveillance, navigation, manipulation, or voice, which makes it simple to assemble intelligence for any robot. Every block is simulation ready, so engineers can validate workflows inside the dApp or in Gazebo before deployment. Once tested, the same blocks run seamlessly on hardware through QARC or directly on the robot. This approach turns robotics AI into a system of reusable and composable parts. From perception to decisions, Qognix makes intent driven autonomy practical and scalable
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QACE Dynamics | Qognix Rule Engine Qognix is built on a library of modular AI blocks that can be reused across many applications. Each block has NLP built in, so it understands natural instructions and converts them into intents, rules, and actions. The blocks are organized as tiles for specific domains such as surveillance, navigation, manipulation, or voice, which makes it simple to assemble intelligence for any robot. Every block is simulation ready, so engineers can validate workflows inside the dApp or in Gazebo before deployment. Once tested, the same blocks run seamlessly on hardware through QARC or directly on the robot. This approach turns robotics AI into a system of reusable and composable parts. From perception to decisions, Qognix makes intent driven autonomy practical and scalable

Qace Dynamics

36,922 просмотров • 10 месяцев назад

Autonomous driving and obstacles avoidance at drift speeds, challenging the limits of what is possible! In this demo, our vehicle can be seen performing #autonomousdriving at very high speeds, causing it to both skid and drift at turns, while also avoiding obstacles. At such speeds, given the inherent dynamics of the vehicle platform used, it is very easy for the vehicle to topple. The #reinforcementlearning based motion planning and decision making framework that is being demoed here is tasked with ensuring obstacles avoidance without compromising on the speed, to an extent possible, and to drive the vehicle as fast as possible. This is evident towards the end of the video, where it can be seen that our vehicle avoided static obstacles while drifting.This demonstrates the level of sophistication and agility in our framework to ensure proper control of the #autonomousvehicles at high-speeds. The use cases are many; to begin with, our generic off-roads autonomous driving research focuses on enabling autonomous navigation in previously unknown and unseen environments, while ensuring mathematical completeness guarantees. Such agility can also help on-road autonomous vehicles to deal with unforeseeable corner cases or sudden appearance of obstacles in its tracks, at high-speeds. Our underlying research at Swaayatt Robots is still far from over, and over the next 3-4 months, we will be demonstrating abstract representation being learned by our multi-RL agents based framework (under progress) to ensure computation of the cost of the terrain without any labelled data, where multiple agents learn to control / regulate different aspects of autonomous navigation, to ensure safe and robust navigation, both on- and off-roads. All the people on the ground, who participated in the demo, were trained safety professionals. #deeplearning #MachineLearning #Robotics
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Autonomous driving and obstacles avoidance at drift speeds, challenging the limits of what is possible! In this demo, our vehicle can be seen performing #autonomousdriving at very high speeds, causing it to both skid and drift at turns, while also avoiding obstacles. At such speeds, given the inherent dynamics of the vehicle platform used, it is very easy for the vehicle to topple. The #reinforcementlearning based motion planning and decision making framework that is being demoed here is tasked with ensuring obstacles avoidance without compromising on the speed, to an extent possible, and to drive the vehicle as fast as possible. This is evident towards the end of the video, where it can be seen that our vehicle avoided static obstacles while drifting.This demonstrates the level of sophistication and agility in our framework to ensure proper control of the #autonomousvehicles at high-speeds. The use cases are many; to begin with, our generic off-roads autonomous driving research focuses on enabling autonomous navigation in previously unknown and unseen environments, while ensuring mathematical completeness guarantees. Such agility can also help on-road autonomous vehicles to deal with unforeseeable corner cases or sudden appearance of obstacles in its tracks, at high-speeds. Our underlying research at Swaayatt Robots is still far from over, and over the next 3-4 months, we will be demonstrating abstract representation being learned by our multi-RL agents based framework (under progress) to ensure computation of the cost of the terrain without any labelled data, where multiple agents learn to control / regulate different aspects of autonomous navigation, to ensure safe and robust navigation, both on- and off-roads. All the people on the ground, who participated in the demo, were trained safety professionals. #deeplearning #MachineLearning #Robotics

Sanjeev Sharma

11,181 просмотров • 1 год назад

Sub-1 hour? Chasing the human half-marathon record? That’s suddenly the conversation around Beijing’s humanoid robot race this Sunday. 🏃‍♂️🤖 A year ago, this event was mostly about finishing. Now the bar is speed, stability, and full-course autonomy. More than 100 teams and 300+ humanoid robots are expected on the line, and nearly 40% of them are entering the autonomous navigation group instead of relying on remote control. The course is not getting easier either. Same 21.0975 km, but with more urban slopes, rolling sections, and park terrain built to expose weak points in control, perception, and endurance. That is what makes this race worth watching. If some of these machines really get close to one hour, the story is no longer “robots can run.” It becomes “humanoids are starting to hold speed, balance, and navigation together over real distance.” And that matters far beyond a race course. This is one of the clearest public tests yet for where humanoid robotics actually stands in hardware, motion control, power systems, and autonomous mobility. Sunday’s headline will be the time. The bigger takeaway will be how much of that pace is still there at kilometer 15, 18, and 21. I want to see who is actually fast, who is truly autonomous, and who can finish without falling apart. That is where the real signal will be.
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Sub-1 hour? Chasing the human half-marathon record? That’s suddenly the conversation around Beijing’s humanoid robot race this Sunday. 🏃‍♂️🤖 A year ago, this event was mostly about finishing. Now the bar is speed, stability, and full-course autonomy. More than 100 teams and 300+ humanoid robots are expected on the line, and nearly 40% of them are entering the autonomous navigation group instead of relying on remote control. The course is not getting easier either. Same 21.0975 km, but with more urban slopes, rolling sections, and park terrain built to expose weak points in control, perception, and endurance. That is what makes this race worth watching. If some of these machines really get close to one hour, the story is no longer “robots can run.” It becomes “humanoids are starting to hold speed, balance, and navigation together over real distance.” And that matters far beyond a race course. This is one of the clearest public tests yet for where humanoid robotics actually stands in hardware, motion control, power systems, and autonomous mobility. Sunday’s headline will be the time. The bigger takeaway will be how much of that pace is still there at kilometer 15, 18, and 21. I want to see who is actually fast, who is truly autonomous, and who can finish without falling apart. That is where the real signal will be.

RoboHub🤖

209,428 просмотров • 3 месяцев назад

Watch Overland AI's Autonomous Ground Vehicle ULTRA race through the desert. ULTRA moves across a gap from one trail onto another and then charges forward towards an observation post at the National Training Center in the Mojave. This is real-time (1×) video of Overland’s ground autonomy in action. True off-road navigation with onboard sensing and compute.
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Watch Overland AI's Autonomous Ground Vehicle ULTRA race through the desert. ULTRA moves across a gap from one trail onto another and then charges forward towards an observation post at the National Training Center in the Mojave. This is real-time (1×) video of Overland’s ground autonomy in action. True off-road navigation with onboard sensing and compute.

Overland AI

15,385 просмотров • 3 месяцев назад

Autonomous driving through tight, dynamic, stochastic, and adversarial traffic-dynamics on sub-urban roads in India, as well as through partially unstructured environments. This demos showcases the robustness of our motion planning and decision making algorithmic frameworks in enabling #autonomousdriving through seamlessly through such traffic and environmental scenarios. The vehicle starts from a generic open environment at the temple, where there are no traffic-rules to abide by. It then exits the region and assumes a generic autonomous navigation behaviour, negotiating complex traffic scenes. At various points it can be seen that the other vehicles (bikes, autos, bicyclists, and cars) didn't abide by any traffic-rule and moved in crisscross fashion, presenting adversarial scenarios, challenging our autonomous vehicle at Swaayatt Robots to take care of the collision avoidance. This classical motion planning and decision making algorithmic framework is being further scaled up with deep #reinforcementlearning, which will practically solve the sub-urban traffic-dynamics and environment negotiation for #autonomousvehicles in India and throughout the world as well. This demo was done at the Kankali Kali Mata mandir in the city of Bhopal. This demo was a culmination of our prior works and demos: off-roads, on-roads, bidirectional traffic negotiation in single lane roads, and toll-plaza navigation. We have taken up the arduous task of solving the Level-4 autonomous driving by the end of 2024, globally. #machinelearning #deeplearning
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Autonomous driving through tight, dynamic, stochastic, and adversarial traffic-dynamics on sub-urban roads in India, as well as through partially unstructured environments. This demos showcases the robustness of our motion planning and decision making algorithmic frameworks in enabling #autonomousdriving through seamlessly through such traffic and environmental scenarios. The vehicle starts from a generic open environment at the temple, where there are no traffic-rules to abide by. It then exits the region and assumes a generic autonomous navigation behaviour, negotiating complex traffic scenes. At various points it can be seen that the other vehicles (bikes, autos, bicyclists, and cars) didn't abide by any traffic-rule and moved in crisscross fashion, presenting adversarial scenarios, challenging our autonomous vehicle at Swaayatt Robots to take care of the collision avoidance. This classical motion planning and decision making algorithmic framework is being further scaled up with deep #reinforcementlearning, which will practically solve the sub-urban traffic-dynamics and environment negotiation for #autonomousvehicles in India and throughout the world as well. This demo was done at the Kankali Kali Mata mandir in the city of Bhopal. This demo was a culmination of our prior works and demos: off-roads, on-roads, bidirectional traffic negotiation in single lane roads, and toll-plaza navigation. We have taken up the arduous task of solving the Level-4 autonomous driving by the end of 2024, globally. #machinelearning #deeplearning

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.Treasury Secretary Scott Bessent on the Strait of Hormuz: "We are seeing more and more ships go through on a daily basis...but over time, the U.S. is going to retake control of the straits, and there will be freedom of navigation, whether it is through U.S. escorts or a multinational escort."

Rapid Response 47

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Andy Lowery joins me for an outdoor demo of cutting-edge defense technology, showcasing the Leonidas autonomous ground vehicle and its role in modern battlefield protection. Built through a collaboration between Epirus, General Dynamics Land Systems, and Kodiak Defense, this mobile system integrates advanced radar, autonomous navigation, and directed energy capabilities to detect, track, and neutralize drone threats in real time. I need one of these for the studio. 😂 Epirus
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Andy Lowery joins me for an outdoor demo of cutting-edge defense technology, showcasing the Leonidas autonomous ground vehicle and its role in modern battlefield protection. Built through a collaboration between Epirus, General Dynamics Land Systems, and Kodiak Defense, this mobile system integrates advanced radar, autonomous navigation, and directed energy capabilities to detect, track, and neutralize drone threats in real time. I need one of these for the studio. 😂 Epirus

Shawn Ryan

120,980 просмотров • 3 месяцев назад

Cross-embodied robot policies hold the promise of one policy to control all robots. But how far does transfer go? In new work, we study positive transfer between *manipulation* & *navigation* and show that nav data helps manipulation, and vice versa! 🧵 👇
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Cross-embodied robot policies hold the promise of one policy to control all robots. But how far does transfer go? In new work, we study positive transfer between *manipulation* & *navigation* and show that nav data helps manipulation, and vice versa! 🧵 👇

Sergey Levine

42,721 просмотров • 2 лет назад

IRGC-N Commander Tangsiri: The weapon that we will use during the battlefield on the day of war is different from what is shown in the exercise. If at any time of point, the security apparatus decides to close the strait of Hormuz, our forces will implement this in the shortest time possible. — Today: Some parts of the Strait of Hormuz will be closed for a few hours. They’ll be closed for the smart control exercise of the Strait of Hormuz, to observe safety and navigation principles. Ships will still be able to sail through other parts of the Strait of Hormuz.
2:51

IRGC-N Commander Tangsiri: The weapon that we will use during the battlefield on the day of war is different from what is shown in the exercise. If at any time of point, the security apparatus decides to close the strait of Hormuz, our forces will implement this in the shortest time possible. — Today: Some parts of the Strait of Hormuz will be closed for a few hours. They’ll be closed for the smart control exercise of the Strait of Hormuz, to observe safety and navigation principles. Ships will still be able to sail through other parts of the Strait of Hormuz.

Arya - آریا

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LimX Dynamics demonstrated autonomous unboxing with its humanoid robot Oli, requiring almost no human effort. The robot can step out of its crate and become fully operational on its own without manual setup. Multiple robots can be activated together and controlled through a single system, saving time and effort.
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LimX Dynamics demonstrated autonomous unboxing with its humanoid robot Oli, requiring almost no human effort. The robot can step out of its crate and become fully operational on its own without manual setup. Multiple robots can be activated together and controlled through a single system, saving time and effort.

Space and Technology

31,905 просмотров • 4 месяцев назад

Andrej Karpathy: "Tesla actually does use a lot of expensive sensors, just at training time." Tesla amortizes the cost of LiDAR and other expensive techniques for offline mapping across millions of production vehicles that use vision-only for navigation — can this give them a leg up on the quest towards L5 autonomy vs. Waymo's approach?
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Andrej Karpathy: "Tesla actually does use a lot of expensive sensors, just at training time." Tesla amortizes the cost of LiDAR and other expensive techniques for offline mapping across millions of production vehicles that use vision-only for navigation — can this give them a leg up on the quest towards L5 autonomy vs. Waymo's approach?

Bilawal Sidhu

310,390 просмотров • 1 год назад

Autonomous driving through very dense dynamic traffic, with extremely tight-complex-stochastic traffic-dynamics on sub-urban roads, connecting to an open ground, with absolutely zero traffic-rules. This is the most heavily cluttered environment where we have tested our #autonomousdriving technology, presenting many of the adversarial negotiation scenarios as well, throughout the autonomous navigation task. This demo was done at the Mata Baglamukhi Madir campus in the city of Nalkheda, in MP, India, and was done in the presence of heavy police forces deployed that day on the ground, as can be seen in our demo. Our autonomous vehicle starts from the temple with a generic open environment, with zero traffic rules, with very narrow corridors created out of barricades for vehicles movement by the security forces. In the corridor no two vehicles can pass through at the same time, and our vehicle was tasked with driving through this corridor, while negotiating its way from any traffic, two-wheelers, or pedestrians it faces, with dense presence of bikes and cars on either side, presenting a very challenging environment for #autonomousvehicles. The vehicle exits the open area, and then assumes generic dual lane navigation, avoiding both static and dynamic obstacles, before encountering a police check-post, where the vehicle is supposed to wait if the barricade is closed, and proceed if open. Upon exiting the checkpost, the vehicle negotiates a traffic-intersection with stochastic and adversarial driving behaviour of other vehicles on the road. Our vehicle continuously faced heavily cluttered traffic scene, where entities on the road can execute a random driving pattern, making the decision making task very challenging. We did the demo over a period of two days, successfully executing multiple (30+) trials in this setting. This demo was again a culmination of our prior works and demos: Kankali Kali Mata demo, on-roads, bidirectional negotiation capability on single lane roads, and open environment Level-5 negotiation capability as showcased in our Toll-Plaza demo. We again scaled up classical decision making and motion planning algorithmic framework, to adapt to such a level of density of obstacles on the road. This framework is further being scaled up with #reinforcementlearning and unsupervised #deeplearning at Swaayatt Robots. We will again do a demo in the month of June here, showcasing autonomously acquired skills to pave the way for Level-5 autonomous driving, and to solve the Level-4 autonomy problem by the end of 2024. #MachineLearning
5:59

Autonomous driving through very dense dynamic traffic, with extremely tight-complex-stochastic traffic-dynamics on sub-urban roads, connecting to an open ground, with absolutely zero traffic-rules. This is the most heavily cluttered environment where we have tested our #autonomousdriving technology, presenting many of the adversarial negotiation scenarios as well, throughout the autonomous navigation task. This demo was done at the Mata Baglamukhi Madir campus in the city of Nalkheda, in MP, India, and was done in the presence of heavy police forces deployed that day on the ground, as can be seen in our demo. Our autonomous vehicle starts from the temple with a generic open environment, with zero traffic rules, with very narrow corridors created out of barricades for vehicles movement by the security forces. In the corridor no two vehicles can pass through at the same time, and our vehicle was tasked with driving through this corridor, while negotiating its way from any traffic, two-wheelers, or pedestrians it faces, with dense presence of bikes and cars on either side, presenting a very challenging environment for #autonomousvehicles. The vehicle exits the open area, and then assumes generic dual lane navigation, avoiding both static and dynamic obstacles, before encountering a police check-post, where the vehicle is supposed to wait if the barricade is closed, and proceed if open. Upon exiting the checkpost, the vehicle negotiates a traffic-intersection with stochastic and adversarial driving behaviour of other vehicles on the road. Our vehicle continuously faced heavily cluttered traffic scene, where entities on the road can execute a random driving pattern, making the decision making task very challenging. We did the demo over a period of two days, successfully executing multiple (30+) trials in this setting. This demo was again a culmination of our prior works and demos: Kankali Kali Mata demo, on-roads, bidirectional negotiation capability on single lane roads, and open environment Level-5 negotiation capability as showcased in our Toll-Plaza demo. We again scaled up classical decision making and motion planning algorithmic framework, to adapt to such a level of density of obstacles on the road. This framework is further being scaled up with #reinforcementlearning and unsupervised #deeplearning at Swaayatt Robots. We will again do a demo in the month of June here, showcasing autonomously acquired skills to pave the way for Level-5 autonomous driving, and to solve the Level-4 autonomy problem by the end of 2024. #MachineLearning

Sanjeev Sharma

332,952 просмотров • 2 лет назад

JUST IN: DoorDash unveiled Dot, its first autonomous delivery robot built for the streets. • travels up to 20 mph, runs 6–8 hours on a swappable battery • 4.5 ft tall, ~350 pounds, carries up to 30 lbs (6 pizza boxes) • 8 cameras, radar & 3 LiDAR sensors for navigation • already making deliveries in Phoenix (Tempe & Mesa) • orchestrated with human drivers & drones via DoorDash’s AI platform DoorDash is rolling out to 1.5M people this year 🤯
1:04

JUST IN: DoorDash unveiled Dot, its first autonomous delivery robot built for the streets. • travels up to 20 mph, runs 6–8 hours on a swappable battery • 4.5 ft tall, ~350 pounds, carries up to 30 lbs (6 pizza boxes) • 8 cameras, radar & 3 LiDAR sensors for navigation • already making deliveries in Phoenix (Tempe & Mesa) • orchestrated with human drivers & drones via DoorDash’s AI platform DoorDash is rolling out to 1.5M people this year 🤯

Ritwik Pavan

154,146 просмотров • 10 месяцев назад

🧭Sequence Navigation Tool🧭 Got a lot of level sequences with complex hierarchies? Then the new Sequence Navigation Tool is for you! Use the tool to: 🔵Easily jump to different Shots & Subsequences in an hierarchy tree panel 🔵Filter & Search Bindings and Tracks across the hierarchy 🔵Customize column views to view properties and toggle actions on Shots & SubSequences Download UE today to check it out:
1:21

🧭Sequence Navigation Tool🧭 Got a lot of level sequences with complex hierarchies? Then the new Sequence Navigation Tool is for you! Use the tool to: 🔵Easily jump to different Shots & Subsequences in an hierarchy tree panel 🔵Filter & Search Bindings and Tracks across the hierarchy 🔵Customize column views to view properties and toggle actions on Shots & SubSequences Download UE today to check it out:

Unreal Engine

16,754 просмотров • 11 месяцев назад

LimX just released the TRON 2 Autonomous Mobile Manipulation Kit, and demonstrated the possibilities of modular design. One use case demo I really like shows dexterous hand picking up takeout, the wheeled-legged robot smoothly climbing a flight of stairs, and then handing it off to another TRON2 It combines the speed of wheels with the high mobility of legs,3D perception, autonomous navigation, whole-body motion control+manipulation… --- Not every building has an elevator. A wheeled-legged TRON 2 could become a truly reliable life assistant, especially for elderly people living alone.
0:44

LimX just released the TRON 2 Autonomous Mobile Manipulation Kit, and demonstrated the possibilities of modular design. One use case demo I really like shows dexterous hand picking up takeout, the wheeled-legged robot smoothly climbing a flight of stairs, and then handing it off to another TRON2 It combines the speed of wheels with the high mobility of legs,3D perception, autonomous navigation, whole-body motion control+manipulation… --- Not every building has an elevator. A wheeled-legged TRON 2 could become a truly reliable life assistant, especially for elderly people living alone.

CyberRobo

37,861 просмотров • 15 дней назад