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I'm launching dingbotics: speeding up RL for robotics go to dingbotics dot com to check out the latest model I've trained. this will work on your phone's web browser built with mujoco, nvidia warp, pufferlib, raylib & Rich the Kid

kache

341,261 subscribers

39,265 views • 3 months ago •via X (Twitter)

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success!! trained with pufferlib with my 4090 this was trained against a captured cudagraph from mujoco warp. the training loop runs at 250k SPS. this is 2 hours to train Still not as good as the baseline. But its mine! Same obs & action set up as mujoco playground

success!! trained with pufferlib with my 4090 this was trained against a captured cudagraph from mujoco warp. the training loop runs at 250k SPS. this is 2 hours to train Still not as good as the baseline. But its mine! Same obs & action set up as mujoco playground

kache

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For this hackathon I taught a robot to beat you in Jenga. It sims every possible move, picks the block that's the best move, then an RL policy pushes it out. Built in MuJoCo & Three.js, running on a real AgileX Robotics arm.

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jonathanhawkins

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1. Go to zotero(dot)org. Download and install it on your computer. You will also need to install Zotero Connector. This is a tool that connects Zotero with your web browser. (I am using Chrome.) Click on "Install Chrome Connector" and add it your browser. Once it's added, click on the puzzle-like icon and pin the Zotero Connector to your browser.

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Mushtaq Bilal, PhD

53,524 views • 2 years ago

hi all, excited to join! i'm building an expressive mini "shoggoth" robot which will eventually be hooked up to gpt4o realtime voice. i'm currently working on the low-level policies, which are trained in a mujoco simulation with RL. to delay working on raw-pixels for now, i trained a pose-estimation model using deeplabcut and triangulate the position in 3d space using the stereo cameras. eventually, i'll use gpt4o's tool calling capabilities to activate several of these policies (closed and open loop) based on the dialog flow! captions: manual actuation of the tentacle / 3d pose estimation / target design

hi all, excited to join! i'm building an expressive mini "shoggoth" robot which will eventually be hooked up to gpt4o realtime voice. i'm currently working on the low-level policies, which are trained in a mujoco simulation with RL. to delay working on raw-pixels for now, i trained a pose-estimation model using deeplabcut and triangulate the position in 3d space using the stereo cameras. eventually, i'll use gpt4o's tool calling capabilities to activate several of these policies (closed and open loop) based on the dialog flow! captions: manual actuation of the tentacle / 3d pose estimation / target design

Matthieu LC

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Big news! Our AI browser for SEO is now available for free to everyone on MacOS and Windows. SERP Lens is out of beta and is the go-to workspace for SEOs and their AI agents. Purpose-built for the work of search, the application runs on live web data and is ready for what comes next. Here are some of the reasons why you need to test it out now: • Built in agentic functions • Select your favorite frontier AI model • Utilize skills trained by top consultants • Connect any MCP server & GSC data • Beautifully designed interface For a short time only, you can test out the AI with complimentary credits within the “Pro” trial; then it's $39 p/mo for continued access (can use the browser + some VPN locations for free). Our dev team, led by Sam Underwood 🇬🇧, has done some incredible work here, so make sure to check it out now and let us know what you think! Take a look for yourself:

Big news! Our AI browser for SEO is now available for free to everyone on MacOS and Windows. SERP Lens is out of beta and is the go-to workspace for SEOs and their AI agents. Purpose-built for the work of search, the application runs on live web data and is ready for what comes next. Here are some of the reasons why you need to test it out now: • Built in agentic functions • Select your favorite frontier AI model • Utilize skills trained by top consultants • Connect any MCP server & GSC data • Beautifully designed interface For a short time only, you can test out the AI with complimentary credits within the “Pro” trial; then it's $39 p/mo for continued access (can use the browser + some VPN locations for free). Our dev team, led by Sam Underwood 🇬🇧, has done some incredible work here, so make sure to check it out now and let us know what you think! Take a look for yourself:

Brodie Clark

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I'm really warming up to KC Concepcion. Check out this interview feature about him and struggling while growing up with a stutter. Phenomenal kid. Zero WR Diva. He's the anti-OBJ. And go check out his open letter to NFL GMs on the Players Tribune if you haven't yet.

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Roberto Shenanigans

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Summer storms combined with high volume are leading to high levels of air travel disruption this week. DOT will continue our work to support passengers & promote resiliency in the airline system. If you’re flying, be sure to check out to know your rights.

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With the latest version of qgis2threejs in QGIS you can set your 3D scenes to autoplay in the browser, following any path you like (as you can see I need to work on this!) - here's the live web page example shown in the video #QGIS

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Alasdair Rae

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Getting on HUMBL Social has never been easier. Here is a quick demo from start to finish with the new web flow on from your phone’s native browser. ***This process will work for both iOS as well as Android*** #HUMBL

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TAP, Inc.

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Jensen just announced Newton, an open-source physics engine for robotics simulation developed by NVIDIA, Google DeepMind, and Disney Research. ⦿ Built on NVIDIA Warp, it offers GPU-accelerated performance and compatibility with frameworks like MuJoCo and Isaac Lab. ⦿ Features differentiable physics, extensibility for multiphysics simulations, and integration with OpenUSD for flexible data modeling. ⦿ Newton addresses the sim-to-real gap by creating realistic virtual environments for robot training and testing. ⦿ Disney Research is using Newton to power next-gen entertainment robots, including Star Wars-inspired BDX droids.

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🖨️ It took over a year and lots of help from Claude Code but I have now been able to create a real dot matrix printer on the web that prints from Windows 3.11 in your browser: Here's the test page: I couldn't get the LPT1 port to work but COM2 did work (the dial up model already uses COM1), the JS receives the COM2 serial data (just raw print data) and then prints it Next is to make the dot matrix printer better looking (kinda 3d like on pieter dot com) and have the paper also flowing in 3d out of the printer and integrate it on the page My idea was that it might work because we also capture the COM1 data (from the dial up modem) and send it to a Websocket as a dial up connection, so we should be able to capture the COM2 traffic too but I didn't know how, but now I do IT WORKS!! 😊

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@levelsio

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Historically, RL policies for robots have been trained in synthetic, untextured environments, limiting perceptive policies to depth images where the sim-to-real gap is manageable. RGB has always had more potential, but leveraging it to train policies in simulation remained an open problem. Partnering with Niantic Spatial 🌎 and NVIDIA Robotics, we built a pipeline that addresses exactly that. We can now scan a real deployment site with off-the-shelf hardware, reconstruct it into a photorealistic Gaussian splat, and run massively parallel RL training. The policies trained in our Gym environment then transfer zero-shot to the real robot and environments they were trained for. This enables faster deployment of more capable and robust policies for the end user. The new resulting capabilities are a big step towards solving sim-to-real and also apply well beyond navigation. Read the full technical breakdown on our blog; link in the comments. #HumanoidRobots #Flexion #NianticSpatial #NVIDIA

Historically, RL policies for robots have been trained in synthetic, untextured environments, limiting perceptive policies to depth images where the sim-to-real gap is manageable. RGB has always had more potential, but leveraging it to train policies in simulation remained an open problem. Partnering with Niantic Spatial 🌎 and NVIDIA Robotics, we built a pipeline that addresses exactly that. We can now scan a real deployment site with off-the-shelf hardware, reconstruct it into a photorealistic Gaussian splat, and run massively parallel RL training. The policies trained in our Gym environment then transfer zero-shot to the real robot and environments they were trained for. This enables faster deployment of more capable and robust policies for the end user. The new resulting capabilities are a big step towards solving sim-to-real and also apply well beyond navigation. Read the full technical breakdown on our blog; link in the comments. #HumanoidRobots #Flexion #NianticSpatial #NVIDIA

Flexion

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