Video yükleniyor...

Video Yüklenemedi

Ana Sayfaya Dön

Enabling autonomous vehicles perceive their environment using only off-the-shelf cameras has been a long term research objective at Swaayatt Robots. This demo highlights the capabilities of our on-road perception system which is able to detect obstacles, road boundaries, lane markers in images, as well as compute depth of the...

sanjeevs_iitr's profile picture

Sanjeev Sharma

6,014 subscribers

18,478 görüntüleme • 1 yıl önce •via X (Twitter)

SanatEğitimBilim & Teknoloji
LIVE

Anya Rossi• Live Now

Private livecam show

11 Yorum

GDP profil fotoğrafı
GDP1 yıl önce

@swaayatt Proud of you guys. Would love to help anyways.

ARK Electronics profil fotoğrafı
ARK Electronics1 yıl önce

Excited about the latest tech for your drone product? Our NDAA-compliant, US-made flight controllers are designed to accelerate your path to market and provide a solid platform for developing your autonomous software. Check them out! #Drones #UAV #UAS #Robotics #MadeInUSA

Ktri. profil fotoğrafı
Ktri.1 yıl önce

This is profound and excellent. The work you are doing is not just for (cars) . Your cars become robots collecting data . The greater the scalability of this the greater the use of this in the normal environment. Cars, robots and other scalability are all possible. Really happy for you. OM

Vicharak profil fotoğrafı
Vicharak1 yıl önce

@swaayatt Real time compute will require real-time reconfigurable edge computers, you can check out Vaaman. ( )

Suresh Kumar R profil fotoğrafı
Suresh Kumar R1 yıl önce

@swaayatt Awesome. Keep it up

pranav13 profil fotoğrafı
pranav131 yıl önce

@swaayatt Great going

JSR profil fotoğrafı
JSR1 yıl önce

@swaayatt Congratulations.

Sanjeev Sharma profil fotoğrafı
Sanjeev Sharma1 yıl önce

@swaayatt Thank you so much! :)

திலீப் profil fotoğrafı
திலீப்1 yıl önce

@centerofright @swaayatt Can it detect invincible, dark, unpainted, fat ass speed breakers laid by rejervasion kilass babus and contractor chelas!

C S Yogananda profil fotoğrafı
C S Yogananda1 yıl önce

@swaayatt Good work👏

AK profil fotoğrafı
AK1 yıl önce

@swaayatt 👍👍

Benzer Videolar

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
6:35

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

Sanjeev Sharma

68,144 görüntüleme • 4 ay önce

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

332,875 görüntüleme • 2 yıl önce

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
3:38

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 görüntüleme • 1 yıl önce

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
3:19

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

Sanjeev Sharma

186,204 görüntüleme • 2 yıl önce

Autonomous vehicle learning to dodge traffic, performing stochastic adversarial negotiation. On 27th August we had representatives from the Suzuki Motor Corporation's autonomous department, Genki Maeda (Department Manager, AD Platform Development), Karachi Nobunari (Department General Manager, Advance Technology Development Department) and Ronit Kumar (Suzuki Innovation Center) visit us to test our #autonomousdriving technology. This was a high-stakes demo, where we asked our engineering team (including the founder, Sanjeev Sharma) to ride two wheeler and to cut the path of our autonomous vehicle at random / at will, in a live demo, creating an adversarial scenario, where it is the sole responsibility of our autonomous vehicle to dodge obstacles and prevent accidents. Over the years, we have been building autonomous driving technology to enable negotiation of adversarial-complex-stochastic traffic dynamics. This demo is only a short trailer of what is being developed and what is going to come next. We made the vehicle first negotiate randomly placed static vehicles, bikes and cones on the road. Then in the next section of the road, our engineering team started cutting the path of autonomous vehicle at random, and let it take care of obstacles avoidance and negotiation, balancing aggressiveness and passivity. These algorithmic frameworks are being scaled up to achieve Level-4 and Level-5 autonomous driving, in the most complex of traffic situations imaginable in the world for #autonomousvehicles, i.e., Indian traffic on Indian roads, to conquer this space globally. The speed of the vehicle was kept low in this demo, keeping in mind the safety of the guests. The core motion planning and decision making algorithm in the demo utilized one #reinforcementlearning agent. There is going to be another demo on similar lines next week, on an extended stretch of a road. #deeplearning #India
4:55

Autonomous vehicle learning to dodge traffic, performing stochastic adversarial negotiation. On 27th August we had representatives from the Suzuki Motor Corporation's autonomous department, Genki Maeda (Department Manager, AD Platform Development), Karachi Nobunari (Department General Manager, Advance Technology Development Department) and Ronit Kumar (Suzuki Innovation Center) visit us to test our #autonomousdriving technology. This was a high-stakes demo, where we asked our engineering team (including the founder, Sanjeev Sharma) to ride two wheeler and to cut the path of our autonomous vehicle at random / at will, in a live demo, creating an adversarial scenario, where it is the sole responsibility of our autonomous vehicle to dodge obstacles and prevent accidents. Over the years, we have been building autonomous driving technology to enable negotiation of adversarial-complex-stochastic traffic dynamics. This demo is only a short trailer of what is being developed and what is going to come next. We made the vehicle first negotiate randomly placed static vehicles, bikes and cones on the road. Then in the next section of the road, our engineering team started cutting the path of autonomous vehicle at random, and let it take care of obstacles avoidance and negotiation, balancing aggressiveness and passivity. These algorithmic frameworks are being scaled up to achieve Level-4 and Level-5 autonomous driving, in the most complex of traffic situations imaginable in the world for #autonomousvehicles, i.e., Indian traffic on Indian roads, to conquer this space globally. The speed of the vehicle was kept low in this demo, keeping in mind the safety of the guests. The core motion planning and decision making algorithm in the demo utilized one #reinforcementlearning agent. There is going to be another demo on similar lines next week, on an extended stretch of a road. #deeplearning #India

Swaayatt Robots

22,200 görüntüleme • 9 ay önce

Autonomous driving through extremely-tight-dynamic environments with complex, stochastic, and adversarial traffic-dynamics, or simply through an absolute chaos, on sub-urban unstructured roads in India. This kind of traffic and environment has never been attempted in the history of #autonomousdriving. There were no traffic-rules to abide by on this road, other than to perform a left-sided avoidance, if the other obstacles follow the same, else the vehicle will have to change its plan in a stochastic manner, in several of the adversarial multi-agent negotiation settings encountered throughout the autonomous navigation. This demos tested our motion planning and decision making framework to its limits, showcasing its robustness in negotiating such traffic-dynamics with ease. This demo was done on mostly a very narrow road, suited mostly for one-way navigation, but as is customary in India, bidirectional traffic is active on such narrow roads. It can be seen throughout navigation that the incoming vehicles didn't allow any gaps for our #AutonomousVehicles, forcing it to negotiate passively-aggressively its own path through the chaos. Furthermore, obstacles overtaking us didn't follow any rules either, and zig-zagged and moved in crisscross fashion, challenging our motion and behaviour planning software, which negotiated all such scenarios with ease. There were only two points where our vehicle came to halt, when two girls on a two-wheeler didn't stop and just kept on navigating, despite our vehicle being closer to the narrow passage and it having the right of way, and despite a bike being parked over there by someone, making it a very challenging scenario both for the humans and for the decision making autonomous agent(s). This demo was done in the Awadhpuri area, on the Durga Mata road. This framework was last shown in relatively much sparser traffic in our Kankali Kali Mata demo last month. It is being scaled further with deep unsupervised and #reinforcementlearning , and in the coming weeks, it will play a critical role in our endeavour to solving the Level-4 autonomy problem by the end of the year. This kind of traffic negotiation has never been attempted by any autonomous driving company ever. While a 90-degree turn is usually discussed as a corner case in the West, our autonomous vehicle negotiated a blind 90-degree corner, with traffic, with ease. #deeplearning #MachineLearning Swaayatt Robots
6:38

Autonomous driving through extremely-tight-dynamic environments with complex, stochastic, and adversarial traffic-dynamics, or simply through an absolute chaos, on sub-urban unstructured roads in India. This kind of traffic and environment has never been attempted in the history of #autonomousdriving. There were no traffic-rules to abide by on this road, other than to perform a left-sided avoidance, if the other obstacles follow the same, else the vehicle will have to change its plan in a stochastic manner, in several of the adversarial multi-agent negotiation settings encountered throughout the autonomous navigation. This demos tested our motion planning and decision making framework to its limits, showcasing its robustness in negotiating such traffic-dynamics with ease. This demo was done on mostly a very narrow road, suited mostly for one-way navigation, but as is customary in India, bidirectional traffic is active on such narrow roads. It can be seen throughout navigation that the incoming vehicles didn't allow any gaps for our #AutonomousVehicles, forcing it to negotiate passively-aggressively its own path through the chaos. Furthermore, obstacles overtaking us didn't follow any rules either, and zig-zagged and moved in crisscross fashion, challenging our motion and behaviour planning software, which negotiated all such scenarios with ease. There were only two points where our vehicle came to halt, when two girls on a two-wheeler didn't stop and just kept on navigating, despite our vehicle being closer to the narrow passage and it having the right of way, and despite a bike being parked over there by someone, making it a very challenging scenario both for the humans and for the decision making autonomous agent(s). This demo was done in the Awadhpuri area, on the Durga Mata road. This framework was last shown in relatively much sparser traffic in our Kankali Kali Mata demo last month. It is being scaled further with deep unsupervised and #reinforcementlearning , and in the coming weeks, it will play a critical role in our endeavour to solving the Level-4 autonomy problem by the end of the year. This kind of traffic negotiation has never been attempted by any autonomous driving company ever. While a 90-degree turn is usually discussed as a corner case in the West, our autonomous vehicle negotiated a blind 90-degree corner, with traffic, with ease. #deeplearning #MachineLearning Swaayatt Robots

Sanjeev Sharma

125,218 görüntüleme • 2 yıl önce

Presenting autonomous driving in complex, stochastic and adversarial traffic-dynamics, on the roads in #India. Over the course of last year, we enabled #autonomousdriving in conditions and in situations no one in the autonomous driving industry considered was possible, and in a country like India, where the contemporary belief was that #autonomousvehicles are an impossibility. With the closure of 2023, we present autonomous driving at a very large scale, in the city of Bhopal in India. In this demo our autonomous vehicle at Swaayatt Robots can be seen negotiating the surrounding traffic with ease, where it had to deal with their stochastic and adversarial driving patterns, like suddenly switching lanes and appearing all of a sudden in our vehicle's current driving lane, without adherence to the traffic rules. This demo was a culmination of the cutting-edge research we have been doing, and the technologies we have been developing, over the years, which we showcased throughout in our demos in 2023 -- campus autonomous driving (February), off-roads autonomous driving (April and September), tight-stochastic traffic negotiation (August and September), bidirectional traffic negotiation on a single lane road (October), large-scale city level demo (November), and Toll-Plaza negotiation (December). In 2024, we will scale our technology commercially, and bring it to North America and Europe, to topple this trillion dollar industry, and will also scale our technology throughout India. Wishing everyone a very Happy New Year! #deeplearning #reinforcementlearning #MachineLearning Elon Musk PMO India Narendra Modi Nitin Gadkari DARPA DRDO
6:50

Presenting autonomous driving in complex, stochastic and adversarial traffic-dynamics, on the roads in #India. Over the course of last year, we enabled #autonomousdriving in conditions and in situations no one in the autonomous driving industry considered was possible, and in a country like India, where the contemporary belief was that #autonomousvehicles are an impossibility. With the closure of 2023, we present autonomous driving at a very large scale, in the city of Bhopal in India. In this demo our autonomous vehicle at Swaayatt Robots can be seen negotiating the surrounding traffic with ease, where it had to deal with their stochastic and adversarial driving patterns, like suddenly switching lanes and appearing all of a sudden in our vehicle's current driving lane, without adherence to the traffic rules. This demo was a culmination of the cutting-edge research we have been doing, and the technologies we have been developing, over the years, which we showcased throughout in our demos in 2023 -- campus autonomous driving (February), off-roads autonomous driving (April and September), tight-stochastic traffic negotiation (August and September), bidirectional traffic negotiation on a single lane road (October), large-scale city level demo (November), and Toll-Plaza negotiation (December). In 2024, we will scale our technology commercially, and bring it to North America and Europe, to topple this trillion dollar industry, and will also scale our technology throughout India. Wishing everyone a very Happy New Year! #deeplearning #reinforcementlearning #MachineLearning Elon Musk PMO India Narendra Modi Nitin Gadkari DARPA DRDO

Sanjeev Sharma

95,416 görüntüleme • 2 yıl önce

In this demo we extend our prior work on obstacles avoidance at aggressive speeds, showcasing our Thar based autonomous vehicle navigating at near drift speeds, progressing towards our endeavour of Level-5 autonomy. Our autonomous vehicle at Swaayatt Robots was tasked with avoidance of traffic cones on the road, placed in a zig-zag fashion, at aggressive speeds. The location of the marked cones was not known to the planner beforehand. The #autonomousdriving task, i.e., motion planning (time parametrized trajectory computation) and decision making, was made even more challenging by restricting the AI agents to not act on obstacles unless they are within 24m radius. Level-5 #autonomousvehicles should be able to react quickly to overtake, or to avoid, any sudden unforeseeable obstacle or pedestrian on the road to avoid fatalities -- a capability demonstrated by our novel motion planning and decision making algorithmic framework over here. Our previous demo showcased our Bolero based platform consistently keeping speeds beyond 45 KMPH for most part, slowing down to only 39 KMPH at one point. Given Thar has lesser body roll, our framework successfully kept speeds well above 47 KMPH (even at the points of avoidance of obstacles), with speeds reaching as high as 55 KMPH. A typical human driver would feel uncomfortable at speeds beyond 40 KMPH in such as scenario. The entire algorithmic framework with 5 classical (one #reinforcementlearning-) agents , runs at 800+ Hz on a regular i7 processor, single thread. This algorithmic framework is being further scaled up with end-to-end deep reinforcement learning, and will be showcased in the month of March. #deeplearning #machinelearning #motionplanning
1:18

In this demo we extend our prior work on obstacles avoidance at aggressive speeds, showcasing our Thar based autonomous vehicle navigating at near drift speeds, progressing towards our endeavour of Level-5 autonomy. Our autonomous vehicle at Swaayatt Robots was tasked with avoidance of traffic cones on the road, placed in a zig-zag fashion, at aggressive speeds. The location of the marked cones was not known to the planner beforehand. The #autonomousdriving task, i.e., motion planning (time parametrized trajectory computation) and decision making, was made even more challenging by restricting the AI agents to not act on obstacles unless they are within 24m radius. Level-5 #autonomousvehicles should be able to react quickly to overtake, or to avoid, any sudden unforeseeable obstacle or pedestrian on the road to avoid fatalities -- a capability demonstrated by our novel motion planning and decision making algorithmic framework over here. Our previous demo showcased our Bolero based platform consistently keeping speeds beyond 45 KMPH for most part, slowing down to only 39 KMPH at one point. Given Thar has lesser body roll, our framework successfully kept speeds well above 47 KMPH (even at the points of avoidance of obstacles), with speeds reaching as high as 55 KMPH. A typical human driver would feel uncomfortable at speeds beyond 40 KMPH in such as scenario. The entire algorithmic framework with 5 classical (one #reinforcementlearning-) agents , runs at 800+ Hz on a regular i7 processor, single thread. This algorithmic framework is being further scaled up with end-to-end deep reinforcement learning, and will be showcased in the month of March. #deeplearning #machinelearning #motionplanning

Sanjeev Sharma

12,811 görüntüleme • 1 yıl önce

Swaayatt Robots is a Bhopal-based autonomous vehicle startup. They just released a demo video showing their self-driving technology at work on a single-lane, two-way road. During this demo the driverless car encountered a herd of cows, a blind corner, and a daredevil cyclist.
0:55

Swaayatt Robots is a Bhopal-based autonomous vehicle startup. They just released a demo video showing their self-driving technology at work on a single-lane, two-way road. During this demo the driverless car encountered a herd of cows, a blind corner, and a daredevil cyclist.

Runtime

10,596 görüntüleme • 8 ay önce

#WATCH | J&K: An Indian Army Major says, "In our Operation Sindoor, 'Akashteer' system has played the most important role. This system is a centrally automated command and control system that is integrated with all the air forces and the army radars... The enemy's drones came to our locations in a very large number. At that time, our Akashteer system declared them as hostile, and according to their location, the nearest weapon system was designated that you have to destroy them. Due to which we effectively destroyed all the drones that came into our territory. It was 100% effective. We destroyed all the drones and our mission and objective was not to let the enemy accomplish any of their objectives, we have been successful in it. This system is made in India. It is helping us to destroy enemy drones and aircraft in a good way..."
4:54

#WATCH | J&K: An Indian Army Major says, "In our Operation Sindoor, 'Akashteer' system has played the most important role. This system is a centrally automated command and control system that is integrated with all the air forces and the army radars... The enemy's drones came to our locations in a very large number. At that time, our Akashteer system declared them as hostile, and according to their location, the nearest weapon system was designated that you have to destroy them. Due to which we effectively destroyed all the drones that came into our territory. It was 100% effective. We destroyed all the drones and our mission and objective was not to let the enemy accomplish any of their objectives, we have been successful in it. This system is made in India. It is helping us to destroy enemy drones and aircraft in a good way..."

ANI

23,790 görüntüleme • 1 yıl önce

#BreakingNews: Beyoncé caught worshipping Lucifer in her song “Alien Superstar” have you ever checked the sample ? “The DJ booth is conducting a troubleshoot test of the entire system. Somehow, while the party was in progress, an unidentified frequency has been existing in the system for some time. And while many of you have been made too brainwashed to comprehend, this frequency is, and has become a threat to our society as we know it. This frequency has been used by a secret society in conjunction with Lucifer to lure and prey on innocent partygoers. With hypnotism, syncroprism, tricknology, lies, scandal, and pornography. While the party is still in progress we will keep you updated on our current status” This is only a test
2:35

#BreakingNews: Beyoncé caught worshipping Lucifer in her song “Alien Superstar” have you ever checked the sample ? “The DJ booth is conducting a troubleshoot test of the entire system. Somehow, while the party was in progress, an unidentified frequency has been existing in the system for some time. And while many of you have been made too brainwashed to comprehend, this frequency is, and has become a threat to our society as we know it. This frequency has been used by a secret society in conjunction with Lucifer to lure and prey on innocent partygoers. With hypnotism, syncroprism, tricknology, lies, scandal, and pornography. While the party is still in progress we will keep you updated on our current status” This is only a test

Keeping Culture Alive

26,856 görüntüleme • 2 ay önce

🚨 Apple joins the robotics race! Researchers from Carnegie Mellon University and Apple have developed a robot collision avoidance system. They called it ARMOR and they implemented it on the Fourier GR-1 robot. What is ARMOR? 👘 ARMOR is a system that helps humanoid robots "see" their surroundings better by using small, smart depth sensors placed on their arms. It solves the problem of blind spots and occlusions in traditional robot perception, making robots much safer and more efficient. It uses Time-of-Flight (ToF) sensors to detect obstacles, providing a near-complete view of the robot’s environment. 🔭 A smart software powered by transformer-based AI learns from human movements to help robots plan their actions dynamically. ARMOR reduces collisions by an impressive 63.7%, making robots significantly better at navigating crowded or complex spaces. The system is fast as well! 💨 ARMOR-Policy cuts computational time by 26× compared to traditional planning methods, ensuring robots can act quickly. P.S. Thoughts on this? The research paper is in the comments! ~~~ ♻️ RT to help 1 robot find a new workplace.
1:31

🚨 Apple joins the robotics race! Researchers from Carnegie Mellon University and Apple have developed a robot collision avoidance system. They called it ARMOR and they implemented it on the Fourier GR-1 robot. What is ARMOR? 👘 ARMOR is a system that helps humanoid robots "see" their surroundings better by using small, smart depth sensors placed on their arms. It solves the problem of blind spots and occlusions in traditional robot perception, making robots much safer and more efficient. It uses Time-of-Flight (ToF) sensors to detect obstacles, providing a near-complete view of the robot’s environment. 🔭 A smart software powered by transformer-based AI learns from human movements to help robots plan their actions dynamically. ARMOR reduces collisions by an impressive 63.7%, making robots significantly better at navigating crowded or complex spaces. The system is fast as well! 💨 ARMOR-Policy cuts computational time by 26× compared to traditional planning methods, ensuring robots can act quickly. P.S. Thoughts on this? The research paper is in the comments! ~~~ ♻️ RT to help 1 robot find a new workplace.

Lukas Ziegler

169,366 görüntüleme • 1 yıl önce

Our first mission at Borg is to help restore America's industrial and manufacturing strength with the help of our Ai integrated robots. In this video, you'll see our autonomous pallet jack and our modular humanoid robot Borg 01 in action. Borg 01 modular design enables autonomous switching between a bipedal and wheeled base, as well as interchangeability of end effectors. We're gearing up to launch three robots in late Q2 including the pallet jack, the humanoid Borg 01, and one more yet to be announced. Stay tuned. Built in Detroit.
0:59

Our first mission at Borg is to help restore America's industrial and manufacturing strength with the help of our Ai integrated robots. In this video, you'll see our autonomous pallet jack and our modular humanoid robot Borg 01 in action. Borg 01 modular design enables autonomous switching between a bipedal and wheeled base, as well as interchangeability of end effectors. We're gearing up to launch three robots in late Q2 including the pallet jack, the humanoid Borg 01, and one more yet to be announced. Stay tuned. Built in Detroit.

borg™

53,840 görüntüleme • 1 yıl önce

#WATCH | Delhi Environment Minister Manjinder Singh Sirsa writes to the Commission for Air Quality Management to place on hold the direction which mandates the denial of fuel to End-of-Life (EOL) vehicles in Delhi He says, "We will clean the environment of Delhi and will not allow the vehicles of Delhi to be confiscated. This is the resolution of our Delhi Chief Minister, Rekha Gupta ji, towards the people of Delhi, this is her promise to the people of Delhi... It will be better if this system is implemented in the entire NCR and then it is implemented in Delhi. We are also looking for a solution that the vehicles should not be banned on the basis of age, but should be banned considering their pollution capacity..."
2:25

#WATCH | Delhi Environment Minister Manjinder Singh Sirsa writes to the Commission for Air Quality Management to place on hold the direction which mandates the denial of fuel to End-of-Life (EOL) vehicles in Delhi He says, "We will clean the environment of Delhi and will not allow the vehicles of Delhi to be confiscated. This is the resolution of our Delhi Chief Minister, Rekha Gupta ji, towards the people of Delhi, this is her promise to the people of Delhi... It will be better if this system is implemented in the entire NCR and then it is implemented in Delhi. We are also looking for a solution that the vehicles should not be banned on the basis of age, but should be banned considering their pollution capacity..."

ANI

57,895 görüntüleme • 11 ay önce

Wheelbot: Conceptual Autonomous Tire System for Versatile Mobility The Wheelbot is a conceptual autonomous tire and wheel system designed to navigate a range of terrains, including urban settings. This concept explores the potential for future mobility solutions in which wheels can detach from vehicles and function independently as robotic units. Credit: Hankook Tire #hankook #tyres #formulaone #formula1 #engineering #techology -------------------------------- Stay ahead of the curve! Follow us now on our WhatsApp ( and Telegram ( channels and stay updated about the cutting edge.
0:31

Wheelbot: Conceptual Autonomous Tire System for Versatile Mobility The Wheelbot is a conceptual autonomous tire and wheel system designed to navigate a range of terrains, including urban settings. This concept explores the potential for future mobility solutions in which wheels can detach from vehicles and function independently as robotic units. Credit: Hankook Tire #hankook #tyres #formulaone #formula1 #engineering #techology -------------------------------- Stay ahead of the curve! Follow us now on our WhatsApp ( and Telegram ( channels and stay updated about the cutting edge.

Wevolver

50,433 görüntüleme • 5 ay önce

Klobuchar on Abrego Garcia: "The charges will be dealt with in the justice system. That's how this works. Of course, they should've done that at the get go and not wrongfully deported him, and handled the charges in our country. In the end, to me, this is about the rule of law."
2:08

Klobuchar on Abrego Garcia: "The charges will be dealt with in the justice system. That's how this works. Of course, they should've done that at the get go and not wrongfully deported him, and handled the charges in our country. In the end, to me, this is about the rule of law."

Aaron Rupar

70,435 görüntüleme • 1 yıl önce

It's is an honour to take on the role of a mother ... as a mother of Pakistan, We have the responsibility to guide our generations to concepts of HARAM and HALAL. And I, for one, will never back off from the stance on qom e lut and lgtv according to the Quran and Sunnah. And I will always call out any one on media who is misleading & lying to our youth. We will continue to raise our voices to protect the family system that Islam has cut out for us. This is the foundation of a sound and functional system of governance and society. Everyone is welcome to join in.
1:26

Sensitive content

It's is an honour to take on the role of a mother ... as a mother of Pakistan, We have the responsibility to guide our generations to concepts of HARAM and HALAL. And I, for one, will never back off from the stance on qom e lut and lgtv according to the Quran and Sunnah. And I will always call out any one on media who is misleading & lying to our youth. We will continue to raise our voices to protect the family system that Islam has cut out for us. This is the foundation of a sound and functional system of governance and society. Everyone is welcome to join in.

Maria.B: Woman=Adult Human Female

91,600 görüntüleme • 2 yıl önce

“Autonomous vehicles force policymakers to rethink the rules of the road—when the ‘driver’ is a system, not a person,” said Justin Kintz, Head of Global Public Policy at Waymo, at The Hill’s AI in America Summit. He highlighted the challenge of aligning federal, state, and local frameworks with rapidly learning AI systems as driverless technology moves from testing into real-world deployment. Explore more: #TheHillTech #AutonomousVehicles #TechPolicy
2:07

“Autonomous vehicles force policymakers to rethink the rules of the road—when the ‘driver’ is a system, not a person,” said Justin Kintz, Head of Global Public Policy at Waymo, at The Hill’s AI in America Summit. He highlighted the challenge of aligning federal, state, and local frameworks with rapidly learning AI systems as driverless technology moves from testing into real-world deployment. Explore more: #TheHillTech #AutonomousVehicles #TechPolicy

The Hill Events

12,027 görüntüleme • 5 ay önce