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Agile Training

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36,618 просмотров • 3 месяцев назад •via X (Twitter)

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🇬🇧 and 🇸🇪 have taken their partnership one step further. Agile Combat Employment is stronger with trusted partners 🤝. Joint training ensures aircraft can be serviced anywhere, anytime 🔧✈️. #WeAreNATO #FlyandFight #StrongerTogether
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🇬🇧 and 🇸🇪 have taken their partnership one step further. Agile Combat Employment is stronger with trusted partners 🤝. Joint training ensures aircraft can be serviced anywhere, anytime 🔧✈️. #WeAreNATO #FlyandFight #StrongerTogether

Royal Air Force

26,798 просмотров • 1 год назад

Transforming our workforce. Reshaping our training capacity. Today, U.S. Coast Guard released a Request for Information in coordination with Homeland Security to identify possible locations for an additional training center. As the Coast Guard modernizes through Force Design 2028 and grows by up to 15,000 personnel, we need additional training capacity to shape a more agile, capable, and responsive maritime fighting force. 🔗Learn more: -Force Design 2028: -Training Center RFI: -Press Release:
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Transforming our workforce. Reshaping our training capacity. Today, U.S. Coast Guard released a Request for Information in coordination with Homeland Security to identify possible locations for an additional training center. As the Coast Guard modernizes through Force Design 2028 and grows by up to 15,000 personnel, we need additional training capacity to shape a more agile, capable, and responsive maritime fighting force. 🔗Learn more: -Force Design 2028: -Training Center RFI: -Press Release:

U.S. Coast Guard

56,729 просмотров • 8 месяцев назад

#OperationalTraining #Jointness #Synergy #RanbankuraDivision under the aegis of #ChetakCorps conducted integrated training with #IndianAirForce in form of seamless Special Heliborne Operations (SHBO) at #MahajanFieldFiringRanges. The training validated interoperability in an intense combat environment. An agile & adaptive force - prepared, integrated and ready to unleash to fight & win under all circumstances. #IndianArmy #SaptaShaktiCommand #ChetakCorps #RanbankuraDivison #Aviators ADG PI - INDIAN ARMY Chetak Corps HQ IDS PRO Jaipur MoD prodefencechandigarh
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#OperationalTraining #Jointness #Synergy #RanbankuraDivision under the aegis of #ChetakCorps conducted integrated training with #IndianAirForce in form of seamless Special Heliborne Operations (SHBO) at #MahajanFieldFiringRanges. The training validated interoperability in an intense combat environment. An agile & adaptive force - prepared, integrated and ready to unleash to fight & win under all circumstances. #IndianArmy #SaptaShaktiCommand #ChetakCorps #RanbankuraDivison #Aviators ADG PI - INDIAN ARMY Chetak Corps HQ IDS PRO Jaipur MoD prodefencechandigarh

SouthWesternCommand_IA

12,417 просмотров • 8 месяцев назад

When I got to 90kg+ I needed to work twice as hard to be as agile as I was when I was at 80kg. It’s rare to find someone that can deadlift 260kilos and do muscle ups. That’s the beauty of hybrid training. Keep working.
0:28

When I got to 90kg+ I needed to work twice as hard to be as agile as I was when I was at 80kg. It’s rare to find someone that can deadlift 260kilos and do muscle ups. That’s the beauty of hybrid training. Keep working.

Ebuka Chidube👑

13,841 просмотров • 1 месяц назад

It’s the little wins that make a big difference. I couldn’t dip this low when I started training at the beginning of the year. To see my mobility coming back and my body becoming a bit more agile is encouraging. I wanna see what I will cook by staying locked in for a whole year.
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It’s the little wins that make a big difference. I couldn’t dip this low when I started training at the beginning of the year. To see my mobility coming back and my body becoming a bit more agile is encouraging. I wanna see what I will cook by staying locked in for a whole year.

Don Billiato

121,400 просмотров • 1 месяц назад

HumanX: a scalable framework that converts single monocular human videos into agile, generalizable interaction skills for humanoid robots without task-specific rewards. Core parts: - XGen: retargets human motion + synthesizes diverse physically plausible training data (via physics-driven object trajectories and augmentation). - XMimic: unified imitation pipeline for robust generalization. Shows promising results for both Blind (no external sensors) and MoCap modes.
1:08

HumanX: a scalable framework that converts single monocular human videos into agile, generalizable interaction skills for humanoid robots without task-specific rewards. Core parts: - XGen: retargets human motion + synthesizes diverse physically plausible training data (via physics-driven object trajectories and augmentation). - XMimic: unified imitation pipeline for robust generalization. Shows promising results for both Blind (no external sensors) and MoCap modes.

The Humanoid Hub

20,358 просмотров • 5 месяцев назад

A new member joins the European humanoid robot team. German company Agile Robotics has launched its first industrial humanoid robot, Agile One, featuring intuitive human-robot interaction, dexterous hands (for grasping small screws and touching the screen), and AI-driven operation trained in the real world. It performs tasks such as material collection, handling, picking and placing, loading and unloading machine tools, tool handling, and precision operations. The emergence of humanoid robots by industrial robot companies is a natural progression; they are designed for areas inaccessible to traditional robotic arms and industrial robots, extending operational systems and enhancing business collaboration capabilities. Agile One will be manufactured in Germany in early 2026 and deployed for on-site training at customers to enhance its AI model capabilities. Soon, Europe will enter its second wave of automation driven by intelligent technology.
1:32

A new member joins the European humanoid robot team. German company Agile Robotics has launched its first industrial humanoid robot, Agile One, featuring intuitive human-robot interaction, dexterous hands (for grasping small screws and touching the screen), and AI-driven operation trained in the real world. It performs tasks such as material collection, handling, picking and placing, loading and unloading machine tools, tool handling, and precision operations. The emergence of humanoid robots by industrial robot companies is a natural progression; they are designed for areas inaccessible to traditional robotic arms and industrial robots, extending operational systems and enhancing business collaboration capabilities. Agile One will be manufactured in Germany in early 2026 and deployed for on-site training at customers to enhance its AI model capabilities. Soon, Europe will enter its second wave of automation driven by intelligent technology.

CyberRobo

72,567 просмотров • 8 месяцев назад

#OperationalTraining #ChetakCorps The #GandivKnights under the aegis of #GandivDivision validated its combat readiness during an Integrated Combat Command Exercise. During the excercise formation forged & validated operational drills, operational logistics & efficacy of a tactical level drone organisation. The training ensured that agile, adaptive & battle ready formation is ready to strike anytime & anywhere. #IndianArmy #SaptaShaktiCommand #GandivKnights #FutureReadyForce ADG PI - INDIAN ARMY SouthWesternCommand_IA PRO Jaipur MoD
1:16

#OperationalTraining #ChetakCorps The #GandivKnights under the aegis of #GandivDivision validated its combat readiness during an Integrated Combat Command Exercise. During the excercise formation forged & validated operational drills, operational logistics & efficacy of a tactical level drone organisation. The training ensured that agile, adaptive & battle ready formation is ready to strike anytime & anywhere. #IndianArmy #SaptaShaktiCommand #GandivKnights #FutureReadyForce ADG PI - INDIAN ARMY SouthWesternCommand_IA PRO Jaipur MoD

Chetak Corps

20,323 просмотров • 6 месяцев назад

#GeneralUpendraDwivedi, #COAS visited the Military College of Telecommunications and Engineering, #MCTE and reviewed initiatives driving advancements in defence technology and data-centric warfare. He toured the NFS Advance Training Centre, a pan-India tri-services facility delivering high tech, curriculum driven training aligned with the strategic goals of #MultiDomainOperations and #NetworkCentricity, in consonance with the #YearofNetworkingAndDataCentricity. #COAS also visited the Drone Training Node, reflecting the Indian Army’s vision of “Eagle on the Arm,” where field-deployable AI and drone-enabled solutions supporting the shift from voice-centric to data-centric operations were demonstrated. He commended all ranks of the institute for their pivotal contribution in shaping agile, technologically adept and future-ready warriors for the Indian Army. #IndianArmy रक्षा मंत्री कार्यालय/ RMO India Ministry of Defence, Government of India Army Training Command, Indian Army
0:37

#GeneralUpendraDwivedi, #COAS visited the Military College of Telecommunications and Engineering, #MCTE and reviewed initiatives driving advancements in defence technology and data-centric warfare. He toured the NFS Advance Training Centre, a pan-India tri-services facility delivering high tech, curriculum driven training aligned with the strategic goals of #MultiDomainOperations and #NetworkCentricity, in consonance with the #YearofNetworkingAndDataCentricity. #COAS also visited the Drone Training Node, reflecting the Indian Army’s vision of “Eagle on the Arm,” where field-deployable AI and drone-enabled solutions supporting the shift from voice-centric to data-centric operations were demonstrated. He commended all ranks of the institute for their pivotal contribution in shaping agile, technologically adept and future-ready warriors for the Indian Army. #IndianArmy रक्षा मंत्री कार्यालय/ RMO India Ministry of Defence, Government of India Army Training Command, Indian Army

ADG PI - INDIAN ARMY

267,074 просмотров • 4 месяцев назад

Enough with LLMs - exciting things are happening in the world of atoms. This is Stanford ALOHA, a low-cost and agile robot platform. The whole system is open-source (!!): hardware design, CAD models for 3D printing, simulator, and training code. Time to graft a physical arm onto GPTs 🦾 Led by my friend Tony Zhao at Stanford, and advised by Chelsea Finn Sergey Levine Vikash Kumar. Project page: If you don't want to 3D print your own components, you can buy the setup at
1:46

Enough with LLMs - exciting things are happening in the world of atoms. This is Stanford ALOHA, a low-cost and agile robot platform. The whole system is open-source (!!): hardware design, CAD models for 3D printing, simulator, and training code. Time to graft a physical arm onto GPTs 🦾 Led by my friend Tony Zhao at Stanford, and advised by Chelsea Finn Sergey Levine Vikash Kumar. Project page: If you don't want to 3D print your own components, you can buy the setup at

Jim Fan

818,510 просмотров • 3 лет назад

We are thrilled to share our breakthrough research on "Agile Flight from Pixels without State Estimation," to be presented and live-demonstrated at #RSS2024 next week! You heard well: no state estimation means no explicit visual localization, no SLAM, no VIO, and no IMU! Paper: Video (Narrated): Last year, we demonstrated that #ReinforcementLearning (RL) policies could outperform world-champion drone-racing pilots using the same quadrotor hardware; however, unlike human pilots, these policies continuously estimated an explicit state from known gate positions, the camera feed, and inertial measurements (IMU). In this new work, we tackle the challenge of learning vision-based drone racing using an end-to-end reinforcement learning approach that eliminates the need for IMU data or explicit state estimation. Like professional pilots, we go directly from images to control commands. The training is facilitated by an asymmetric actor-critic with access to privileged information. To overcome the computational complexity during image-based RL training, we use an appropriate sensor representation, which can be efficiently simulated during training without rendering images. We achieve agile flight at speeds up to 40 km/h with accelerations up to 2 g's. Although our demonstration focuses on drone racing, we believe that our method has an impact beyond drone racing and can serve as a foundation for future research into real-world applications in structured environments. Besides the paper presentation, we will also give a live demo next Tuesday and Wednesday between and hrs at TU Delft: Reference: Ismail Geles*, Leonard Bauersfeld*, Angel Romero, Jiaxu Xing, Davide Scaramuzza "Demonstrating Agile Flight from Pixels without State Estimation" Robotics: Science and Systems (RSS), 2024. Kudos to Ismail Geles Leonard Bauersfeld Ángel Romero Jiaxu Xing! University of Zurich UZH Science UZH Space Hub Aerial Core AUTOASSESS European Research Council (ERC)
1:24

We are thrilled to share our breakthrough research on "Agile Flight from Pixels without State Estimation," to be presented and live-demonstrated at #RSS2024 next week! You heard well: no state estimation means no explicit visual localization, no SLAM, no VIO, and no IMU! Paper: Video (Narrated): Last year, we demonstrated that #ReinforcementLearning (RL) policies could outperform world-champion drone-racing pilots using the same quadrotor hardware; however, unlike human pilots, these policies continuously estimated an explicit state from known gate positions, the camera feed, and inertial measurements (IMU). In this new work, we tackle the challenge of learning vision-based drone racing using an end-to-end reinforcement learning approach that eliminates the need for IMU data or explicit state estimation. Like professional pilots, we go directly from images to control commands. The training is facilitated by an asymmetric actor-critic with access to privileged information. To overcome the computational complexity during image-based RL training, we use an appropriate sensor representation, which can be efficiently simulated during training without rendering images. We achieve agile flight at speeds up to 40 km/h with accelerations up to 2 g's. Although our demonstration focuses on drone racing, we believe that our method has an impact beyond drone racing and can serve as a foundation for future research into real-world applications in structured environments. Besides the paper presentation, we will also give a live demo next Tuesday and Wednesday between and hrs at TU Delft: Reference: Ismail Geles*, Leonard Bauersfeld*, Angel Romero, Jiaxu Xing, Davide Scaramuzza "Demonstrating Agile Flight from Pixels without State Estimation" Robotics: Science and Systems (RSS), 2024. Kudos to Ismail Geles Leonard Bauersfeld Ángel Romero Jiaxu Xing! University of Zurich UZH Science UZH Space Hub Aerial Core AUTOASSESS European Research Council (ERC)

Davide Scaramuzza

27,917 просмотров • 2 лет назад

Lt Gen Pratik Sharma, #ArmyCdrNC, visited units deployed along the #LineOfControl in Kupwara Sector to assess robustness of anti-infiltration grid and review #OperationalPreparedness. The Army Commander also witnessed a demonstration of innovations successfuly implemented in field conditions to integrate Unmanned Aerial System (UAS) and Counter Unmanned Aerial System (CUAS) with Techniques Training and Procedures (TTPs) for enhanced combat capabilities along the Line of Control. He commended all ranks for their battle hardened resilience, indomitable courage & relentless commitment to duty amidst a challenging operating environment. #ArmyCdr stressed upon continuation of persistent endeavours to improve battle drills & skills to become technically adept, mentally robust and tactically agile. #AlwaysInCombat #NationFirst ADG PI - INDIAN ARMY Chinar Corps🍁 - Indian Army
0:36

Lt Gen Pratik Sharma, #ArmyCdrNC, visited units deployed along the #LineOfControl in Kupwara Sector to assess robustness of anti-infiltration grid and review #OperationalPreparedness. The Army Commander also witnessed a demonstration of innovations successfuly implemented in field conditions to integrate Unmanned Aerial System (UAS) and Counter Unmanned Aerial System (CUAS) with Techniques Training and Procedures (TTPs) for enhanced combat capabilities along the Line of Control. He commended all ranks for their battle hardened resilience, indomitable courage & relentless commitment to duty amidst a challenging operating environment. #ArmyCdr stressed upon continuation of persistent endeavours to improve battle drills & skills to become technically adept, mentally robust and tactically agile. #AlwaysInCombat #NationFirst ADG PI - INDIAN ARMY Chinar Corps🍁 - Indian Army

NORTHERN COMMAND - INDIAN ARMY

17,534 просмотров • 2 месяцев назад

MiG-28- 40 Years in the Shadows Happy 40th Birthday to the legendary MiG-28. Forty years ago, Top Gun introduced the world to this sleek black adversary that owned the Danger Zone. On screen it was pure Hollywood fiction, but the aircraft was 100% real: U.S. Navy Northrop F-5E/F Tiger IIs, the same highly agile jets that served as the primary adversary aircraft for TOPGUN school at NAS Miramar. They were used daily to deliver realistic dissimilar air combat training to Navy fighter pilots. Painted matte black with red stars for the film, these nimble fighters brought every intense dogfight sequence to life, including the signature negative-G pushover dives that became instant aviation legend. Big shoutout to the real pilots who strapped in and made it all authentic: • Lt. Michael “Flex” Galpin • Lt. Gregory “Hollywood” Dishart • Lt. James “Jambo” Ray (and the entire TOPGUN adversary cadre). Still diving deep after four decades. #TopGun40 #MiG28 #F5TigerII #RealAdversary #TOPGUN #NegativeGPushover #NavyAviation #40YearsInTheShadows
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MiG-28- 40 Years in the Shadows Happy 40th Birthday to the legendary MiG-28. Forty years ago, Top Gun introduced the world to this sleek black adversary that owned the Danger Zone. On screen it was pure Hollywood fiction, but the aircraft was 100% real: U.S. Navy Northrop F-5E/F Tiger IIs, the same highly agile jets that served as the primary adversary aircraft for TOPGUN school at NAS Miramar. They were used daily to deliver realistic dissimilar air combat training to Navy fighter pilots. Painted matte black with red stars for the film, these nimble fighters brought every intense dogfight sequence to life, including the signature negative-G pushover dives that became instant aviation legend. Big shoutout to the real pilots who strapped in and made it all authentic: • Lt. Michael “Flex” Galpin • Lt. Gregory “Hollywood” Dishart • Lt. James “Jambo” Ray (and the entire TOPGUN adversary cadre). Still diving deep after four decades. #TopGun40 #MiG28 #F5TigerII #RealAdversary #TOPGUN #NegativeGPushover #NavyAviation #40YearsInTheShadows

Aircraft history junky

42,390 просмотров • 2 месяцев назад

We are excited to share our latest work, "Superhuman Safe and Agile Racing through Multi-Agent Reinforcement Learning," done in collaboration with Google DeepMind . Autonomous drones have reached superhuman speed in isolation, but what happens when multiple agents share the same airspace? Paper: Website: Video: Using league-based self-play, we train #ReinforcementLearning agents that race against a diverse, evolving population of opponents. Through this competitive training, sophisticated behaviors emerge without explicit programming: strategic overtaking, proactive collision avoidance, and even awareness of aerodynamic downwash from nearby drones. In real-world multi-player races at speeds exceeding 80kph (50 mph) and accelerations up to 7g, our agents outperform a five-time Swiss national drone racing champion while reducing collision rates by 50% compared to single-agent baselines. Crucially, training against diverse artificial opponents enables zero-shot generalization to human pilots, achieving over 90% race completion in mixed human-AI races with up to four competitors. A key insight: human pilots adopt riskier strategies when trailing, leading to more crashes under competitive pressure. Our learned policies, by contrast, maintain consistent safety margins regardless of race standing, a property essential for deploying autonomous systems alongside humans. Also, the multi-agent self-play policies are more robust than those trained independently, suggesting that training in competitive environments is not only key to winning races but also to learning safer, more reliable autonomy for real-world multi-robot systems. Kudos to Ismail Geles, Leonard Bauersfeld, Markus Wulfmeier! Ismail Geles Leonard Bauersfeld Markus Wulfmeier European Research Council (ERC) UZH IfI University of Zurich UZH Science UZH Space Hub Swiss Robotics NCCR Robotics
1:47

We are excited to share our latest work, "Superhuman Safe and Agile Racing through Multi-Agent Reinforcement Learning," done in collaboration with Google DeepMind . Autonomous drones have reached superhuman speed in isolation, but what happens when multiple agents share the same airspace? Paper: Website: Video: Using league-based self-play, we train #ReinforcementLearning agents that race against a diverse, evolving population of opponents. Through this competitive training, sophisticated behaviors emerge without explicit programming: strategic overtaking, proactive collision avoidance, and even awareness of aerodynamic downwash from nearby drones. In real-world multi-player races at speeds exceeding 80kph (50 mph) and accelerations up to 7g, our agents outperform a five-time Swiss national drone racing champion while reducing collision rates by 50% compared to single-agent baselines. Crucially, training against diverse artificial opponents enables zero-shot generalization to human pilots, achieving over 90% race completion in mixed human-AI races with up to four competitors. A key insight: human pilots adopt riskier strategies when trailing, leading to more crashes under competitive pressure. Our learned policies, by contrast, maintain consistent safety margins regardless of race standing, a property essential for deploying autonomous systems alongside humans. Also, the multi-agent self-play policies are more robust than those trained independently, suggesting that training in competitive environments is not only key to winning races but also to learning safer, more reliable autonomy for real-world multi-robot systems. Kudos to Ismail Geles, Leonard Bauersfeld, Markus Wulfmeier! Ismail Geles Leonard Bauersfeld Markus Wulfmeier European Research Council (ERC) UZH IfI University of Zurich UZH Science UZH Space Hub Swiss Robotics NCCR Robotics

Davide Scaramuzza

14,612 просмотров • 2 месяцев назад

NEWS: DeepMind teaches robots to play soccer. In a paper released today Google's DeepMind details how they used Deep Reinforcement Learning (Deep RL) to train low-cost, miniature humanoid robots in dynamic environments, allowing them to play a simplified one-versus-one (1v1) soccer game. The robots, equipped with 20 actuated joints, were initially trained in simulation using the MuJoCo physics engine. Through this training, they learned robust and dynamic movement skills like rapid fall recovery, walking, turning, and kicking. The robots seamlessly transitioned between these skills, even surpassing expectations, and developed a basic strategic understanding of the game. During matches, the trained robots demonstrated agile skills such as turning, kicking moving balls, and dynamic defensive blocking. They quickly combined these skills, showcasing their adaptability and outperforming scripted baselines. The robots walked faster, got up quicker, and kicked faster than their counterparts. Individual skills were first trained in isolation within the simulation environment, and then composed in a self-play setting. The robots successfully transferred these skills to real-world scenarios. Full paper can be found here:
2:34

NEWS: DeepMind teaches robots to play soccer. In a paper released today Google's DeepMind details how they used Deep Reinforcement Learning (Deep RL) to train low-cost, miniature humanoid robots in dynamic environments, allowing them to play a simplified one-versus-one (1v1) soccer game. The robots, equipped with 20 actuated joints, were initially trained in simulation using the MuJoCo physics engine. Through this training, they learned robust and dynamic movement skills like rapid fall recovery, walking, turning, and kicking. The robots seamlessly transitioned between these skills, even surpassing expectations, and developed a basic strategic understanding of the game. During matches, the trained robots demonstrated agile skills such as turning, kicking moving balls, and dynamic defensive blocking. They quickly combined these skills, showcasing their adaptability and outperforming scripted baselines. The robots walked faster, got up quicker, and kicked faster than their counterparts. Individual skills were first trained in isolation within the simulation environment, and then composed in a self-play setting. The robots successfully transferred these skills to real-world scenarios. Full paper can be found here:

Dave Lee

372,039 просмотров • 3 лет назад

We are excited to share our work “Event-Aided Sharp Radiance Field Reconstruction for Fast-Flying Drones” published in IEEE Transactions on Robotics IEEE Transactions on Robotics (T-RO), which tackles sharp radiance field reconstruction under agile drone motion, where RGB frames are heavily motion-blurred and pose priors become unreliable! 4 years in the making! Code & dataset released! PDF: Code & Dataset: Full Narrated Video: High-speed flight is essential for time- and battery-constrained missions (e.g., inspection, exploration, search & rescue). However, fast motion corrupts visual data with severe motion blur and introduces drift/noise in visual-inertial odometry, making NeRF-based 3D reconstruction particularly brittle. We propose a unified framework that leverages asynchronous #EventCamera streams together with motion-blurred frames to reconstruct high-fidelity radiance fields from agile drone flights. Our key idea is to embed event-image fusion directly into radiance field optimization while jointly refining a shared, continuous-time camera trajectory initialized from event-based VIO. This enables us to recover sharp radiance fields and accurate trajectories without ground-truth supervision during training. We validate our method on synthetic data and on real sequences captured by a drone flying up to 2 m/s. Despite severe blur and noisy pose priors, our method preserves fine scene details and achieves a performance gain of over 50% on real-world data compared to state-of-the-art methods. Kudos to Rong Zou and Marco Cannici! Marco Cannici Reference: Rong Zou*, Marco Cannici*, Davide Scaramuzza Event-Aided Sharp Radiance Field Reconstruction for Fast-Flying Drones IEEE Transactions on Robotics (T-RO), 2026 NCCR Robotics European Research Council (ERC) AUTOASSESS UZH IfI University of Zurich UZH Science Prophesee SynSense UZH Space Hub
1:02

We are excited to share our work “Event-Aided Sharp Radiance Field Reconstruction for Fast-Flying Drones” published in IEEE Transactions on Robotics IEEE Transactions on Robotics (T-RO), which tackles sharp radiance field reconstruction under agile drone motion, where RGB frames are heavily motion-blurred and pose priors become unreliable! 4 years in the making! Code & dataset released! PDF: Code & Dataset: Full Narrated Video: High-speed flight is essential for time- and battery-constrained missions (e.g., inspection, exploration, search & rescue). However, fast motion corrupts visual data with severe motion blur and introduces drift/noise in visual-inertial odometry, making NeRF-based 3D reconstruction particularly brittle. We propose a unified framework that leverages asynchronous #EventCamera streams together with motion-blurred frames to reconstruct high-fidelity radiance fields from agile drone flights. Our key idea is to embed event-image fusion directly into radiance field optimization while jointly refining a shared, continuous-time camera trajectory initialized from event-based VIO. This enables us to recover sharp radiance fields and accurate trajectories without ground-truth supervision during training. We validate our method on synthetic data and on real sequences captured by a drone flying up to 2 m/s. Despite severe blur and noisy pose priors, our method preserves fine scene details and achieves a performance gain of over 50% on real-world data compared to state-of-the-art methods. Kudos to Rong Zou and Marco Cannici! Marco Cannici Reference: Rong Zou*, Marco Cannici*, Davide Scaramuzza Event-Aided Sharp Radiance Field Reconstruction for Fast-Flying Drones IEEE Transactions on Robotics (T-RO), 2026 NCCR Robotics European Research Council (ERC) AUTOASSESS UZH IfI University of Zurich UZH Science Prophesee SynSense UZH Space Hub

Davide Scaramuzza

12,006 просмотров • 4 месяцев назад

I am honored to stand alongside my fellow North-West Governors as we collectively launch the Zamfara State Community Protection Guards in Gusau. This initiative, spearheaded by the Zamfara State Government, aims to enhance the security of lives and property in the region, complementing the efforts of our dedicated security agencies. I commend the agile youths who underwent rigorous training in security operations and peace restoration, crucial skills that will contribute to maintaining lasting peace across Zamfara State. The collaboration of states in our region is vital in addressing security challenges, and this endeavor reflects our commitment to the well-being of our people. I extend my gratitude to the Governors of Sokoto, Katsina, Kano, Jigawa, and Kaduna, represented by Deputy Governor Dr Hadiza Sabuwa Balarabe, for their presence and collective dedication to regional security. Together, we strive to create safer environments for the citizens we serve. On behalf of the people of Kebbi State, I appreciate the opportunity to contribute to this significant effort. I would like to thank the Secretary to Kebbi State Government, Alhaji Yakubu Bala-Tafida, and the other members of our delegation for their unwavering support in this collaborative venture.
1:14

I am honored to stand alongside my fellow North-West Governors as we collectively launch the Zamfara State Community Protection Guards in Gusau. This initiative, spearheaded by the Zamfara State Government, aims to enhance the security of lives and property in the region, complementing the efforts of our dedicated security agencies. I commend the agile youths who underwent rigorous training in security operations and peace restoration, crucial skills that will contribute to maintaining lasting peace across Zamfara State. The collaboration of states in our region is vital in addressing security challenges, and this endeavor reflects our commitment to the well-being of our people. I extend my gratitude to the Governors of Sokoto, Katsina, Kano, Jigawa, and Kaduna, represented by Deputy Governor Dr Hadiza Sabuwa Balarabe, for their presence and collective dedication to regional security. Together, we strive to create safer environments for the citizens we serve. On behalf of the people of Kebbi State, I appreciate the opportunity to contribute to this significant effort. I would like to thank the Secretary to Kebbi State Government, Alhaji Yakubu Bala-Tafida, and the other members of our delegation for their unwavering support in this collaborative venture.

Dr. Nasir Idris

27,609 просмотров • 2 лет назад

We are excited to share our #CORL2024 paper on learning quadrotor obstacle avoidance from the visual stream of a single #eventcamera! Trained entirely in simulation! We demonstrate obstacle avoidance both in the dark and in a forest up to 5m/s. PDF: Video: Project page: Event cameras are sensors that output per-pixel-level intensity changes at microsecond latency resolution; they feature nearly zero motion blur and high dynamic range but produce a very large volume of events under significant ego-motion and further lack a high-fidelity continuous-time sensor model in simulation, making direct #sim2real transfer not possible. By leveraging depth prediction as a pretext task, we pre-train a reactive obstacle avoidance policy with “approximated” simulated events and then fine-tune the perception component with limited events-and-depth real-world data. This technique bridges the sim2real gap for #eventcameras! As at the current state, there is no continuous-time sensor model for event cameras, we hope that this work can finally spur future research leveraging simulation for training event-vision-based policies to create faster, agile robots! Kudos to Anish Bhattacharya, @marcocannic, Vijay Kumar Nikolai Matni UZH Science University of Zurich UZH Space Hub UZH IfI European Research Council (ERC) GRASP Laboratory Penn Engineering
1:25

We are excited to share our #CORL2024 paper on learning quadrotor obstacle avoidance from the visual stream of a single #eventcamera! Trained entirely in simulation! We demonstrate obstacle avoidance both in the dark and in a forest up to 5m/s. PDF: Video: Project page: Event cameras are sensors that output per-pixel-level intensity changes at microsecond latency resolution; they feature nearly zero motion blur and high dynamic range but produce a very large volume of events under significant ego-motion and further lack a high-fidelity continuous-time sensor model in simulation, making direct #sim2real transfer not possible. By leveraging depth prediction as a pretext task, we pre-train a reactive obstacle avoidance policy with “approximated” simulated events and then fine-tune the perception component with limited events-and-depth real-world data. This technique bridges the sim2real gap for #eventcameras! As at the current state, there is no continuous-time sensor model for event cameras, we hope that this work can finally spur future research leveraging simulation for training event-vision-based policies to create faster, agile robots! Kudos to Anish Bhattacharya, @marcocannic, Vijay Kumar Nikolai Matni UZH Science University of Zurich UZH Space Hub UZH IfI European Research Council (ERC) GRASP Laboratory Penn Engineering

Davide Scaramuzza

17,219 просмотров • 1 год назад