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Scientists have developed a neural network-based planning framework that enables robots to navigate complex environments and mazes autonomously. Learn more in Science Robotics:

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25,783 görüntüleme • 1 yıl önce •via X (Twitter)

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Isaac1 yıl önce

@SciRobotics @ScienceMagazine Nature solved maze navigation millions of years ago - slime molds can find optimal paths through labyrinths using distributed intelligence. Now our robots are finally catching up to what Physarum polycephalum mastered through evolution.

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The New Mocha Joe1 yıl önce

@SciRobotics We're fckd

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WhaleX1 yıl önce

@SciRobotics "Scientists are revolutionizing robotics with neural network-based planning, allowing robots to navigate complex environments autonomously."

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Mobile Scanner1 yıl önce

Scan any documents, convert images into text, PDF files, etc. 👍

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Rad Chad1 yıl önce

@SciRobotics Hasn’t this been done before? The Micromouse competition comes to mind

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Scientists have developed SUPER, an autonomous drone that can navigate unknown and cluttered environments safely, which could help future search and rescue efforts. Learn more in Science Robotics:

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Motion planning in complex tasks is hard and still done via slow, explicit, traditional planners. We present a generalist Neural Motion Planner -- a single neural network that plans complex dynamic motions quickly and accurately at test time. Building upon our lab's sim2real efforts, the key idea is to create many complex scenes in simulation and then distill classical motion planner trajectories into a single reactive neural network policy. More details in the thread below! 👇 Open-sourced:
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Motion planning in complex tasks is hard and still done via slow, explicit, traditional planners. We present a generalist Neural Motion Planner -- a single neural network that plans complex dynamic motions quickly and accurately at test time. Building upon our lab's sim2real efforts, the key idea is to create many complex scenes in simulation and then distill classical motion planner trajectories into a single reactive neural network policy. More details in the thread below! 👇 Open-sourced:

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Hopfield recall Spiking Neural network now this one is cool isn't it? -open source, link in comment- This is a Spiking Neural Network (SNN) that can learn and recall patterns through Hebbian learning, similar to a Hopfield network but using biologically-inspired spiking dynamics rather than energy-based settling.

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Implemented a visualization for the neural network today. This is a neural network being trained to learn the snake game in a terminal. It's built in Rust using Ratatui More input details in thread
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