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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 • 11 ay önce •via X (Twitter)

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Isaac11 ay 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 Joe11 ay önce

@SciRobotics We're fckd

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WhaleX11 ay önce

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

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Mobile Scanner11 ay önce

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

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Rad Chad11 ay 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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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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Scientists have developed a rollable robotic structure that is flexible enough to collapse into a compact hub like a tape measure, but also stiff enough to bear heavy loads like 3D printers when extended. Learn more in Science #Robotics:

Science Robotics

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This is how Amazon delivery bot will ring your doorbell. It is done by leveraging the same loco-manipulation framework that enables humanoid robots to move boxes, developed by Amazon's Frontier AI & Robotics team.
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This is how Amazon delivery bot will ring your doorbell. It is done by leveraging the same loco-manipulation framework that enables humanoid robots to move boxes, developed by Amazon's Frontier AI & Robotics team.

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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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San Mateo, California–based Generalist showcases their real-time, end-to-end neural network that controls robots at 100Hz for dexterous manipulation tasks. The model handles fine-motor control, bi-manual coordination, tool use, and generalization across arms and environments.
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San Mateo, California–based Generalist showcases their real-time, end-to-end neural network that controls robots at 100Hz for dexterous manipulation tasks. The model handles fine-motor control, bi-manual coordination, tool use, and generalization across arms and environments.

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Google DeepMind introduces Gemini Robotics 1.5, enabling robots to perceive, plan, think, use tools, and act on complex tasks. The agentic framework comprises: ⦿ Gemini Robotics-ER 1.5 (VLM): Orchestrates high-level embodied reasoning and planning. ⦿ Gemini Robotics 1.5 (VLA): Converts visuals and instructions provided by ER 1.5 into actions.
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Google DeepMind introduces Gemini Robotics 1.5, enabling robots to perceive, plan, think, use tools, and act on complex tasks. The agentic framework comprises: ⦿ Gemini Robotics-ER 1.5 (VLM): Orchestrates high-level embodied reasoning and planning. ⦿ Gemini Robotics 1.5 (VLA): Converts visuals and instructions provided by ER 1.5 into actions.

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Training a Whole-Body Control Foundation Model -- new work from my team at Agility Robotics A neural network for controlling our humanoid robots which is robust to disturbances, can handle heavy objects, and is a powerful platform for learning new whole-body skills learn more in our blog post ->
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Training a Whole-Body Control Foundation Model -- new work from my team at Agility Robotics A neural network for controlling our humanoid robots which is robust to disturbances, can handle heavy objects, and is a powerful platform for learning new whole-body skills learn more in our blog post ->

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New paper introduces NaVILA, a vision-language-action (VLA) model that integrates high-level visual-language understanding and low-level locomotion control. It enables humanoid or quadruped robots to navigate unseen environments with natural language instructions.
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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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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.

echo.hive

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The most frustrating part of imitation learning is collecting huge amounts of teleop data. But why teleop robots when robots can learn by watching us? Introducing Point Policy, a novel framework that enables robots to learn from human videos without any teleop, sim2real, or RL.

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This particle-armored blob robot oozes and tumbles in ways that rigid robots can’t. Learn more in Science Advances:

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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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Agility Robotics is focusing on warehouse logistics. They'll be competing with wheeled bimanual robots that are highly efficient in pick-and-place tasks. One example is Reflex Robotics, a NY-based startup that recently partnered with GXO to deploy robots in warehouses.

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Studying mice and samples from patients, scientists have identified a microRNA-based mechanism that drives atherosclerosis by weakening the integrity of blood vessel walls. Read more in Science Translational Medicine:
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