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I've gotten a mujoco sim RL training loop for a unitree robot at 200k SPS. I'm looking into the physics for friction, contact dynamics. My goal: can I reproduce & beat the mujoco playground RL baselines This is running in my web browser with raylib. Its the baseline

kache

301,184 subscribers

29,728 次观看 • 1 个月前 •via X (Twitter)

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System identification (sysid) is the process of finding the physical parameters that make a simulation match reality. If you're training an RL locomotion policy in simulation, the accuracy of your motor model directly affects how well the policy transfers to the real robot. A recent git commit by Kevin Zakka added a sysid toolbox to MuJoCo which automates this process: you provide recorded motor data and a MuJoCo model, and it optimizes the model parameters to minimize the difference between simulated and real trajectories. For my RobStride Dynamics RS02 QDD motors (17 Nm peak, 7.75:1 gear), I built a Rust tool that sends multi-sine torque excitation at 1 kHz and records position/velocity feedback. I then feed this data into MuJoCo's sysid optimizer.

System identification (sysid) is the process of finding the physical parameters that make a simulation match reality. If you're training an RL locomotion policy in simulation, the accuracy of your motor model directly affects how well the policy transfers to the real robot. A recent git commit by Kevin Zakka added a sysid toolbox to MuJoCo which automates this process: you provide recorded motor data and a MuJoCo model, and it optimizes the model parameters to minimize the difference between simulated and real trajectories. For my RobStride Dynamics RS02 QDD motors (17 Nm peak, 7.75:1 gear), I built a Rust tool that sends multi-sine torque excitation at 1 kHz and records position/velocity feedback. I then feed this data into MuJoCo's sysid optimizer.

David Bar

48,347 次观看 • 3 个月前

This is running in my web browser on my laptop. It's deterministic and running using webgpu Tonight; I have a /goal going where it is going to set up one of my 4090s as the authority server, and then test all the rollback code. Goal is 50k boxes simmed per user, up to 512 users. Massively multiplayer massively rigid body physics online games inside your web browser. Why the hell not, we've got the tokens to spare!

This is running in my web browser on my laptop. It's deterministic and running using webgpu Tonight; I have a /goal going where it is going to set up one of my 4090s as the authority server, and then test all the rollback code. Goal is 50k boxes simmed per user, up to 512 users. Massively multiplayer massively rigid body physics online games inside your web browser. Why the hell not, we've got the tokens to spare!

kache

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I just released a first video deep dive into my LeHome Challenge solution (🥇 sim round, 🥈 real-robot final at ICRA 2026). I already shared what I did and the video series is about why: the reasoning, the alternatives I rejected, and the intuition behind each choice. I split the whole video explanation into 3 parts: Part 1 is about RL for VLAs 👇

I just released a first video deep dive into my LeHome Challenge solution (🥇 sim round, 🥈 real-robot final at ICRA 2026). I already shared what I did and the video series is about why: the reasoning, the alternatives I rejected, and the intuition behind each choice. I split the whole video explanation into 3 parts: Part 1 is about RL for VLAs 👇

Ilia

31,979 次观看 • 20 天前

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

45,587 次观看 • 1 年前

This is how Strike Robot turns Simulation into Reality! One of the biggest challenges in robotics is ensuring that behaviors validated in simulation work reliably in the real world. For this experiment, we reconstructed part of a real laboratory at Eastworlds inside SR Platform. The generated layout was then deployed into MuJoCo. Using SR Agentic, the robot was tasked with finding abnormal objects in a cluttered environment and sending a Telegram notification when detected. Before deployment, everything is validated in simulation.

This is how Strike Robot turns Simulation into Reality! One of the biggest challenges in robotics is ensuring that behaviors validated in simulation work reliably in the real world. For this experiment, we reconstructed part of a real laboratory at Eastworlds inside SR Platform. The generated layout was then deployed into MuJoCo. Using SR Agentic, the robot was tasked with finding abnormal objects in a cluttered environment and sending a Telegram notification when detected. Before deployment, everything is validated in simulation.

Strike Robot

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I spent some time last week vibe-coding "The Beginner's RL Playground" a simple web tool for exploring many basic concepts in reinforcement learning. Users can adjust all sorts of parameters of the algorithm and environment and see the effects of those changes in realtime.

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Chinese consumer electronics maker Honor will unveil its first humanoid robot on March 1 at MWC 2026 in Barcelona. The robot is designed for retail & home service. The company sold a whopping 71 million smartphones in 2025. Looks like a customized Unitree G1 with a 2DoF neck.

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The Humanoid Hub

58,290 次观看 • 5 个月前

Success! 🍎🤖 I can now control Unitree robots on a Mac Mini M4 running macOS with a simple tweak! The unitree_sdk2_python ( thread implementation isn’t essential—getting the DDS subscriber and publisher to work is enough. I replaced Unitree’s Linux thread logic with a simple Python while loop for sensor reading and robot control. Here’s a video of me controlling a Go2 robot on macOS (M4 Mac Mini) using a Python script for low-level control and sensor reading.

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I've a new lease of life since being divorced, I feel like I'm glowing ✨️ I'm looking for a FB 🍆 now the dust has settled 😍 can you guess my age guys ?

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I've a new lease of life since being divorced, I feel like I'm glowing ✨️ I'm looking for a FB 🍆 now the dust has settled 😍 can you guess my age guys ?

kirsty

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Training clips from a couple sessions this week. Looking into a polygamy agreement so I can add the Roger’s squat into my marriage. Just have to get the state & my wife on board. Them damn Mormons may be onto something This is my dose of get the fuck after it for the timeline today. One fact remains: I have to get better 🦍

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No. I started as a noob weightlifting at the age of 50 after my HiiT based gym was forced to close for Covid. My trainer has helped me the entire way. I just hit a 502lbs deadlift at 54yrs old and I have a goal of 552lbs in competition that would beat the record Masters division I'm in. There is no downside for ANY man at ANY age to become physically strong.

No. I started as a noob weightlifting at the age of 50 after my HiiT based gym was forced to close for Covid. My trainer has helped me the entire way. I just hit a 502lbs deadlift at 54yrs old and I have a goal of 552lbs in competition that would beat the record Masters division I'm in. There is no downside for ANY man at ANY age to become physically strong.

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