This NEW AI System SIMULATES REALITY (Genesis A.I Just Changed EVERYTHING!)

00:08:59
https://www.youtube.com/watch?v=5Vc7jm9DzAQ

Résumé

TLDRThe Genesis Project is a groundbreaking tool revolutionizing robotics by merging generative AI with precise physics simulations. This open-source platform allows for creating dynamic, interactive four-dimensional virtual environments to train robots faster and more efficiently. Written in Python, Genesis runs 10-80 times quicker than other high-end tools leveraging GPUs. Unique features include autonomous task designs, real-world physics simulations with incredible detail, soft robotic support, and GPU-parallelized inverse kinematics (IK) solvers. Its versatility spans industries like healthcare, manufacturing, and game development. Opening up new possibilities, Genesis redefines simulation accuracy, speed, and efficiency in robotics and AI.

A retenir

  • 🤖 Genesis blends generative AI with realistic physics to train robots faster.
  • ⚡ It's 10-80 times faster than similar tools, written in Python.
  • 🌍 Creates highly realistic 4D virtual environments for robotics.
  • 📂 Open-source, enabling broad access and innovation.
  • 🦾 Supports soft muscle robotics, a major step forward in simulation.
  • 🧠 Automates robotic training for smarter and more capable systems.
  • 🎮 Simulates diverse phenomena: liquids, smoke, fabric, and more.
  • 📊 Handles complex non-convex collisions for real-world component accuracy.
  • 🎯 GPU acceleration enables massive-scale simulations in milliseconds.
  • 🚀 Genesis reshapes robotics with speed, scalability, and detail.

Chronologie

  • 00:00:00 - 00:08:59

    A revolutionary announcement in robotics propels innovation through The Genesis Project, a cutting-edge generative AI tool combined with real-world physics. By enabling realistic, rapid, and efficient virtual environment creation and training, Genesis reshapes robotics development. This open-source Python-based tool achieves unmatched speed—up to 80 times faster than traditional methods—and can train robots in seconds using a single high-end GPU. With the incorporation of advanced physics including deformable materials, liquids, and soft robotics, as well as features like non-convex collision handling and GPU-parallelized inverse kinematics solvers, Genesis enables scalable and precise robotic simulations, introducing possibilities for wide-ranging applications in industries from healthcare to manufacturing and beyond.

Carte mentale

Vidéo Q&R

  • What is The Genesis Project?

    It's a new tool combining generative AI with realistic physics to create highly accurate virtual environments for training robots.

  • What makes Genesis faster than other simulation tools?

    It’s written in Python, optimized for high-speed computations, and runs 10-80 times faster on GPUs compared to traditional tools.

  • What industries can benefit from Genesis?

    Industries like healthcare, search and rescue, manufacturing, and game development can benefit greatly from realistic robotics simulations.

  • Is Genesis open-source?

    Yes, Genesis is open-source, allowing researchers and developers to access, learn, and create their own projects with the tool.

  • What are some unique features of Genesis?

    Genesis autonomously designs environments, sets tasks, creates rewards, and writes robotic policies, offering unparalleled simulation and training capabilities.

  • Can Genesis simulate soft robotic movements?

    Yes, Genesis supports full soft robotics simulations, including muscle-like movements and interactions with rigid structures.

  • What types of physical phenomena can Genesis simulate?

    Genesis can simulate rigid objects, soft deformables, liquids, smoke, articulated systems, and more.

  • What role does GPU efficiency play in Genesis?

    Its GPU-parallelized IK solver allows it to train thousands of robotic arms simultaneously, immensely speeding up robotic simulations.

  • How does Genesis handle non-convex collisions?

    Genesis has built-in functionality for simulating complex, irregular object collisions, making it suitable for intricate machinery simulations.

  • How long does it take Genesis to train a robot?

    Genesis can train a robot to perform tasks, like walking, in just 26 seconds using a single Nvidia RTX 4090.

Voir plus de résumés vidéo

Accédez instantanément à des résumés vidéo gratuits sur YouTube grâce à l'IA !
Sous-titres
en
Défilement automatique:
  • 00:00:00
    so today we got probably one of the
  • 00:00:02
    craziest announcements this year that
  • 00:00:04
    could truly change the future of
  • 00:00:06
    Robotics this is The Genesis Project
  • 00:00:10
    this is a revolutionary new tool that
  • 00:00:12
    combines the creative power of
  • 00:00:14
    generative AI with the accuracy of real
  • 00:00:17
    world physics imagine being able to
  • 00:00:19
    create virtual environments and train in
  • 00:00:21
    a way that's not just realistic but a
  • 00:00:23
    way that is incredibly fast and
  • 00:00:26
    efficient that's exactly what Genesis
  • 00:00:28
    does now this project has been in
  • 00:00:31
    development for 2 years with over 20
  • 00:00:34
    research labs collaborating to make it a
  • 00:00:36
    reality the result is a powerful physics
  • 00:00:39
    engine that can create Dynamic
  • 00:00:41
    four-dimensional worlds and these worlds
  • 00:00:43
    are fully interactive and Incredibly
  • 00:00:45
    detailed making them perfect for
  • 00:00:47
    developing Advanced Robotics and
  • 00:00:49
    applications in physical AI which is a
  • 00:00:51
    field of teaching machines to understand
  • 00:00:54
    and interact with the physical world now
  • 00:00:56
    Genesis stands out because it's written
  • 00:00:58
    in Python a programming language that
  • 00:01:00
    many people already know and even so
  • 00:01:02
    it's shockingly fast 10 to 80 times
  • 00:01:05
    faster than the other high-end tools
  • 00:01:07
    that rely on powerful gpus like Isaac
  • 00:01:10
    Sim JY and mjx for example it can train
  • 00:01:13
    a robot to walk in just 26 seconds using
  • 00:01:17
    a single Nvidia RTX 4090 which is an
  • 00:01:20
    incredible achievement now the speed
  • 00:01:22
    isn't just for show it's about making
  • 00:01:24
    Cutting Edge technology available to
  • 00:01:26
    more researchers and developers now
  • 00:01:28
    what's crazy about this is that this is
  • 00:01:30
    actually open source this means anyone
  • 00:01:33
    can access this code and learn from it
  • 00:01:35
    and use it to create their own projects
  • 00:01:37
    I think this is probably going to be one
  • 00:01:38
    of the largest developments for robotics
  • 00:01:40
    because one of the main problems that we
  • 00:01:42
    do have in terms of you know trying to
  • 00:01:45
    advance robotics is the data collection
  • 00:01:47
    issue collecting data for robotics is
  • 00:01:49
    tedious because as you all know many
  • 00:01:52
    people currently use tele operation and
  • 00:01:55
    this makes sense because you're
  • 00:01:56
    interacting with the robot in a real
  • 00:01:58
    physical world and of course you can
  • 00:02:00
    collect that data because you know
  • 00:02:02
    exactly where the robot is how it
  • 00:02:04
    interacts with the world and how the
  • 00:02:06
    tasks are achieved successfully or
  • 00:02:09
    unsuccessfully but if we have something
  • 00:02:12
    like this which is a true physics
  • 00:02:14
    simulation something that is very
  • 00:02:16
    accurate in terms of the onetoone
  • 00:02:18
    mapping of the physical world then
  • 00:02:20
    things are going to speed up at a
  • 00:02:22
    ridiculous pace and we've seen before
  • 00:02:24
    with prior projects when we've used
  • 00:02:27
    previous simulations like nvidia's Isaac
  • 00:02:29
    Sim or videos Isaac Jim what that has
  • 00:02:31
    done for the pace of Robotics so if this
  • 00:02:34
    is something that is widely used and is
  • 00:02:37
    remarkably more efficient than those
  • 00:02:39
    other tools from Nvidia then this is
  • 00:02:40
    going to be something that completely
  • 00:02:42
    changes the game and we all know
  • 00:02:45
    companies and countries are really
  • 00:02:47
    really trying to work hard when it comes
  • 00:02:49
    to robotics so with something like this
  • 00:02:52
    being embedded into the ecosystem of AI
  • 00:02:55
    and Robotics we're probably going to see
  • 00:02:58
    things explode overall I'm I mean one of
  • 00:03:00
    the things that we have to understand is
  • 00:03:02
    that the real problem is not the
  • 00:03:04
    humanoid robots platform humanoid robots
  • 00:03:06
    are really successful at doing a variety
  • 00:03:08
    of different tasks the main problem is
  • 00:03:10
    of course the autonomy and ensuring that
  • 00:03:13
    we can get enough data to scale our
  • 00:03:15
    efforts across the world so this is
  • 00:03:17
    going to be something that I think is
  • 00:03:18
    going to be really fascinating
  • 00:03:20
    especially when we start seeing first
  • 00:03:22
    use cases of course right now this is
  • 00:03:24
    something that they have just released
  • 00:03:26
    so it will be a little bit of time
  • 00:03:27
    before we start to see the real world
  • 00:03:29
    implications of this I can imagine there
  • 00:03:31
    are some developers probably already
  • 00:03:33
    hacking away at their Labs figuring out
  • 00:03:35
    the best ways and the best settings to
  • 00:03:38
    get to grips with this kind of software
  • 00:03:40
    so for me this is largely one of the
  • 00:03:42
    craziest AI updates SL robotics updates
  • 00:03:45
    because this has a wide range of
  • 00:03:47
    applications that most of us simply
  • 00:03:49
    cannot fathom and before we get into all
  • 00:03:51
    of the futuristic sci-fi outcomes of
  • 00:03:53
    this let's take a look at how good this
  • 00:03:55
    simulator is and some of the key
  • 00:03:57
    features that differentiates it from any
  • 00:03:59
    software that's existed before now The
  • 00:04:00
    Genesis Project introduces a
  • 00:04:02
    groundbreaking feature a generative
  • 00:04:04
    agent that can handle every step of
  • 00:04:06
    teaching robots how to operate in
  • 00:04:07
    realistic environment entirely on its
  • 00:04:09
    own first it autonomously designs
  • 00:04:11
    virtual environments that mimics real
  • 00:04:13
    world spaces like kitchens living rooms
  • 00:04:15
    and other everyday settings then it
  • 00:04:18
    proposes the tasks for the robots to
  • 00:04:19
    perform such as opening a microwave
  • 00:04:22
    picking up objects or navigating through
  • 00:04:24
    Furniture but it doesn't stop there it
  • 00:04:26
    also creates the reward systems which
  • 00:04:28
    are like the incentives for robots
  • 00:04:30
    teaching them what succcess looks like
  • 00:04:32
    for instance if a robot successfully
  • 00:04:34
    opens a microwave the system gives it a
  • 00:04:36
    reward helping it learn faster and
  • 00:04:38
    better and finally it automates the
  • 00:04:41
    entire process of writing robotic
  • 00:04:43
    policies which are the detailed
  • 00:04:45
    instructions or strategies that guide
  • 00:04:48
    how robots move think and interact with
  • 00:04:50
    their environment that means that robots
  • 00:04:53
    don't just get trained they become
  • 00:04:55
    smarter and more capable of handling
  • 00:04:57
    complex tasks all while developers and
  • 00:05:00
    researchers save time and effort and by
  • 00:05:02
    combining these capabilities Genesis
  • 00:05:04
    sets a new standard for how robots learn
  • 00:05:07
    and adapt to the physical world now
  • 00:05:09
    what's crazy about this physics engine
  • 00:05:11
    that can simulate an incredibly diverse
  • 00:05:13
    range of physical phenomena this engine
  • 00:05:15
    actually combines state-of-the-arts
  • 00:05:17
    physics solvers like mpm material Point
  • 00:05:19
    method SP smooth particle hydrodynamics
  • 00:05:23
    F finite element method rigid body
  • 00:05:25
    Dynamics and pbd among many others and
  • 00:05:28
    this makes it cap of realistically
  • 00:05:30
    simulating virtually anything you can
  • 00:05:33
    imagine from rigid objects to objects
  • 00:05:36
    like metal to soft deformes like cloth
  • 00:05:38
    and rubber to complex materials like
  • 00:05:40
    liquid and smoke and even elastic or
  • 00:05:43
    plastic bodies and it doesn't stop there
  • 00:05:44
    Genesis even supports Advanced
  • 00:05:46
    simulations for articulated systems like
  • 00:05:49
    robot muscles and thin shell materials
  • 00:05:51
    which are crucial for Robotics and
  • 00:05:53
    physical Ai and what this means is that
  • 00:05:56
    Genesis can simulate real world physics
  • 00:05:58
    at an unprecedented level of detail and
  • 00:06:00
    accuracy imagine being able to replicate
  • 00:06:02
    how a piece of fabric Moves In The Wind
  • 00:06:04
    how liquid Splash and flow or how robot
  • 00:06:07
    muscle stretch and contract all in a
  • 00:06:09
    virtual environment these simulations
  • 00:06:11
    are not just for show they play a
  • 00:06:13
    critical role in training AI models and
  • 00:06:15
    robots to interact with the physical
  • 00:06:16
    world in realistic Ways by bringing all
  • 00:06:18
    of these physical capabilities into one
  • 00:06:21
    unified engine Genesis makes it easier
  • 00:06:23
    for developers to research and create
  • 00:06:25
    highly realistic simulations Beyond its
  • 00:06:27
    incredible ability to simulate rigid
  • 00:06:29
    object objects liquids and deformable
  • 00:06:31
    materials Genesis also takes robotics to
  • 00:06:33
    the next level by supporting Soft
  • 00:06:35
    Robotics this is a GameChanger because
  • 00:06:37
    soft robots like those made to mimic
  • 00:06:39
    muscles or organic movements are
  • 00:06:41
    notoriously difficult to simulate
  • 00:06:43
    Genesis bridges that Gap it's the first
  • 00:06:44
    ever platform to provide full support
  • 00:06:46
    for soft muscle simulations and soft
  • 00:06:48
    robot interactions with rigid structures
  • 00:06:50
    what's even better is that Genesis
  • 00:06:52
    includes a configuration system similar
  • 00:06:54
    to urdf which is commonly used in
  • 00:06:57
    robotics this makes it easier than ever
  • 00:06:59
    to design test and refine soft robotic
  • 00:07:02
    systems in Virtual environments for
  • 00:07:04
    example you can simulate how a soft
  • 00:07:05
    robot might crawl or interact with its
  • 00:07:07
    surroundings which has huge potential
  • 00:07:09
    for Industries like healthcare search
  • 00:07:11
    and rescue and more and if Soft Robotics
  • 00:07:14
    wasn't impressive enough Genesis
  • 00:07:15
    continues to push the boundaries with
  • 00:07:17
    its unparalleled computational
  • 00:07:18
    capabilities one standout feature is its
  • 00:07:21
    GPU parallelized ik solver ik or inverse
  • 00:07:25
    kinematics is a critical process in
  • 00:07:27
    robotics that calculates The Joint
  • 00:07:29
    movements needed for a robotic arm to
  • 00:07:31
    reach a specific Target normally this is
  • 00:07:33
    computationally expensive and timec
  • 00:07:35
    consuming especially when dealing with
  • 00:07:36
    multiple robotic systems but Genesis
  • 00:07:39
    takes this to a whole new level it can
  • 00:07:40
    solve ik for an incredible 10,000
  • 00:07:43
    robotic arms like the highly Advanced
  • 00:07:45
    Frank arms simultaneously in under 2
  • 00:07:47
    milliseconds using a single RTX 4090
  • 00:07:50
    this level of speed and efficiency is
  • 00:07:52
    unprecedented and opens up new
  • 00:07:54
    possibilities for large-scale robotic
  • 00:07:56
    simulations Industrial Automation and
  • 00:07:59
    complex research projects with Genesis
  • 00:08:01
    the limits of what we thought was
  • 00:08:02
    possible in robotics are being redefined
  • 00:08:04
    another groundbreaking capability of
  • 00:08:06
    Genesis is its support for Native
  • 00:08:09
    non-convex Collision handling this might
  • 00:08:11
    sound technical but it's a huge deal in
  • 00:08:12
    the world of simulations in simpler
  • 00:08:14
    terms most physics engines struggle to
  • 00:08:16
    handle collisions between objects with
  • 00:08:18
    complex irregular shapes like gears
  • 00:08:21
    chains or any object that isn't smooth
  • 00:08:23
    or simple these shapes are called
  • 00:08:24
    non-convex and accurately simulating
  • 00:08:26
    their collisions requires Advanced
  • 00:08:28
    algorithms is tackles this challenge
  • 00:08:30
    head-on with built-in support for these
  • 00:08:32
    types of interactions this means it can
  • 00:08:34
    accurately simulate how irregularly
  • 00:08:36
    shaped objects bump slide or even
  • 00:08:38
    interlock with one another all while
  • 00:08:40
    maintaining physical realism for example
  • 00:08:42
    if you're working on a robotic system
  • 00:08:44
    that interacts with intricate Machinery
  • 00:08:45
    or components Genesis ensures that every
  • 00:08:48
    collision and movement behaves as it
  • 00:08:50
    would in the real world this capability
  • 00:08:52
    makes Genesis a powerful tool for
  • 00:08:53
    Industries like manufacturing Robotics
  • 00:08:55
    and game development where precise
  • 00:08:57
    Collision handling is crucial
Tags
  • Genesis Project
  • AI
  • robotics
  • physics simulation
  • generative AI
  • open-source
  • soft robotics
  • realistic training
  • GPU efficiency
  • robotic research