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Robots need strong visuo-motor representations to manipulate objects, but it’s hard to learn these using demo data alone. Our #RSS2024 project vastly improves robotic representations, using human affordances mined from Ego4D! w/ Mohan Kumar Srirama Shikhar Bahl Abhinav Gupta

Sudeep Dasari

1,481 subscribers

11,022 просмотров • 1 год назад •via X (Twitter)

Наука и технологии Образование #RSS2024

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Фото профиля Sudeep Dasari

Sudeep Dasari1 год назад

If you’re at @RoboticsSciSys in Delft, drop by our presentation and poster this Wednesday (session 10) at 1:30 PM! Website: Paper:

Фото профиля Yang

Yang1 год назад

Want to learn how practical AI skills and automations for your business and work? Check out our 50+ step-by-step video tutorials 100% FREE 20+ hours of Ai and Automation goodness absolutely free 🥳

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We bust some common myths in visuo-motor pretraining! Our data-centric experiments show: 📷 ImageNet > Ego4D for *self-supervised* robotic pretraining 🏆 Diversity matters more than content: training on a broader image distribution beats SOTA by 30% 📊Sim evals can mislead! 1/n

We bust some common myths in visuo-motor pretraining! Our data-centric experiments show: 📷 ImageNet > Ego4D for self-supervised robotic pretraining 🏆 Diversity matters more than content: training on a broader image distribution beats SOTA by 30% 📊Sim evals can mislead! 1/n

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🤷What if we want to learn from human data... without human data? In our work NIL (No-data Imitation Learning) #CVPR2026, we explore a simple but ambitious question: Can robots learn directly from AI-generated videos without any curated demonstration data? 🔗

🤷What if we want to learn from human data... without human data? In our work NIL (No-data Imitation Learning) #CVPR2026, we explore a simple but ambitious question: Can robots learn directly from AI-generated videos without any curated demonstration data? 🔗

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Our Robotic Foundation Model, trained on data from real-world warehouse operations and simulated scenarios, enables our robots to identify objects, understand 3D spaces, and predict optimal grasping and placement.

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New research from BlackForestLabsAI - Unofficial 🥳 Meet Self-Flow: our self-supervised framework for image, audio, video & world models 🤖 Do generative models really need DINO to learn strong representations? We propose teaching them directly via a joint framework instead 🧵

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Classic childhood activities like tea parties and sword fights with sticks demonstrate the human ability to generate secondary representations, conditions we know aren’t “real” but that we nonetheless engage with. Whether nonhuman animals are capable of these types of representations has been difficult to test. In a new Science study, researchers studied a language-trained bonobo, Kanzi, to see whether he could understand and engage with pretend conditions. Across three different experiments, Kanzi was able to identify pretend objects, demonstrating that he could create a secondary representation and showing that humans are not alone in this ability. Learn more:

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Using monkey brain data to operate robotic arms! 🙉 Project Jenkins explores brain-computer interfaces by decoding neural activity into robotic movement and generating synthetic brain data from motion patterns. Using neural recordings from a macaque monkey named Jenkins, researchers developed models to decode brain signals into robotic arm movements and encode movement data into simulated neural activity. The project utilizes robotic arms and an interactive web console for real-time brain data generation from joystick inputs. The research paves the way for adaptive brain-computer interfaces by modeling brain activity more flexibly. Congrats to authors Dima Yanovsky, and Andrii Zahorodnii! 🦾

Using monkey brain data to operate robotic arms! 🙉 Project Jenkins explores brain-computer interfaces by decoding neural activity into robotic movement and generating synthetic brain data from motion patterns. Using neural recordings from a macaque monkey named Jenkins, researchers developed models to decode brain signals into robotic arm movements and encode movement data into simulated neural activity. The project utilizes robotic arms and an interactive web console for real-time brain data generation from joystick inputs. The research paves the way for adaptive brain-computer interfaces by modeling brain activity more flexibly. Congrats to authors Dima Yanovsky, and Andrii Zahorodnii! 🦾

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Classic childhood activities like tea parties and sword fights with sticks demonstrate the human ability to generate secondary representations, conditions we know aren’t “real” but that we nonetheless engage with. Whether nonhuman animals are capable of these types of representations has been difficult to test. In a new Science study, researchers studied a language-trained bonobo, Kanzi, to see whether he could understand and engage with pretend conditions. Across three different experiments, Kanzi was able to identify pretend objects, demonstrating that he could create a secondary representation and showing that humans are not alone in this ability. Learn more:

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Did you know that Antigravity can be used not just to generate data, but also manipulate that data in Google Sheets using the browser integration?

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Google Antigravity

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