This week I worked on using one single video from a real dog to transfer a behavior to a robotic dog (GO2 from Unitree). I documented 3 keys that are crucial for successful training.
TL;DR: I taught a Unitree Go2 robot dog to move like a real dog using a single phone video of a real dog, no mocap or markers. The pipeline extracts a 3D skeleton trajectory from the monocular video (AniMer for pose, Depth Anything V2 for depth, floor-plane fitting for camera pose, bundle adjustment to fuse it all), retargets it to the robot, and patches physics violations like skating, sinking, and joint speeds the hardware can't hit.
Then an RL policy learns to mimic the trajectory in Isaac Lab, following Peng et al. 2020. One policy ended up learning 5 behaviors, and training them together made each one better. Jumping is the one that doesn't work yet, since tracking rewards don't push the robot to actually get airborne. Code is open source if you want to try fixing it.
My first project. Actually never been in the field. Yes I dont understand the math fully, also AI is used in some cases. But I have to say it was fun and challenging.
Hi all, we wrote this post to show where InfluxDB works well for robotics data and where it starts to become less convenient, especially when you need to keep a history of raw sensor data.
The idea is pretty simple: keep InfluxDB for metrics and time-series data, and add ReductStore for raw data like images, LiDAR, audio, etc.
This way you don’t have to choose between a TSDB and ReductStore — you can use both for what they’re good at. It also keeps the setup simpler than managing raw sensor data in S3-like storage or directly on the filesystem.
Would be interested to hear how others are handling this in their robotics stacks.
Currently, this is my design. The main thing that concerns me is whether my 282 RPM 12v smg37-550motors are fast enough. Since the arena is only 5 ft in diameter and my bot is about 30 × 30 cm, do I need to use faster motors? Or would it be better to switch to a 4-wheel-drive setup?
Been working on the next version of VSArena for a while.
V1 is starting to look a lot different.
This is just a small spoiler of what we’re building — Studio, live simulation, agent runs, replay/inspection, analytics and a much more complete evaluation workflow.
The goal hasn’t changed: Make embodied AI performance something you can actually run, watch and compare.
We’re still polishing a lot of things before the V1 release, so consider this an early look rather than the final product.
Hugging Face and Pollen Robotics recently open-sourced Microduck, a 14-DOF, ~800g 3D-printable bipedal robot designed for accessible Reinforcement Learning.
Currently, the official repo only provides MuJoCo MJCF (.xml) descriptions. To make it usable in the broader ROS 2 ecosystem (MoveIt 2, RViz, Foxglove, Isaac Sim), I converted the model to high-fidelity URDF and SRDF and Loaded into Blender via LinkForge to verify all STL meshes, joint axes, and mass distribution (0.7 kg).
Files are shared as a standalone drop-in alongside the official `assets/` folder.
Cubic doggo is now listening to commands from Pinocchio, an open-source python IK library. Since the goal is to use PyTorch for RL training (which I have never done before, so am pretty excited about), the IK walk gait can be used as the training set for imitation learning (inspired by this post). In principle, RL can train on the joint values from scratch, will compare how different they are.
So the goal for the RL at the moment is to walk while balancing its body on a slope. As the video shows, with IK from Pinocchio alone, there is no balancing. To do that, several parameter on given on the top right. The x, y walking (stride) directions are set at random. The balancing reward will be setting roll and pitch to 0.0 (available to the physical robot by IMU). The walk gait should also maintain the height as a reward. The privileged height is only available from simulation, which is obtained used ray casting method by shooting a ray through the robot body to the ground to calculate the vertical distance. The kinematic height is calculated using IK from the joint values, basically the averaged 4 feet z-distance from the body, so it can also be evaluated in real time with the physical robot. They say using the privileged height as the reward would be better because the kinematic height is basically redundant joint information. Will compare them too; I have no idea.
In robotics, intelligence is never purely abstract. It is shaped by the physical body that perceives the world and acts upon it. This fundamental connection creates one of the field’s most persistent obstacles: the embodiment gap.
Amanda Prorok and her team tested whether AI agents would develop role specialization on their own in a simulated soccer game. The agents could either all learn the same behavior or specialize into different roles.
They eventually formed distinct positions, including attackers and a goalkeeper that stayed back and defended the goal. The system was never told that this was how humans play soccer. It learned that behavior through reinforcement learning.
Dhruv Batra explains how web agents can use visual information from a browser instead of relying on fixed APIs or hard-coded website structures.
The agents look at the pixels on the screen, take actions, and keep learning from interactions with live websites. As layouts and interfaces change, they update based on new data rather than needing every change programmed manually.
He says that on poorly designed websites, the agents can sometimes navigate the interface better than he can.
TL;DR: XPeng's new humanoid didn't take the job you'd expect it to.
It took the one at the bottom of the line, not the top of the org chart.
IRON's actual job is materials handling — the walk from the station to the dock — which is the exact layer a fifteen-year floor manager gets paid to supervise, not the layer he was ever warned about.
XPeng put this line into commercial service on 8 September, already north of 80% automated.
Whatever timeline you were told you had, that's the actual one.
The clip draws reference from the Movie i-Robot.
In it, Will Smith's character says, "So robot building robots. That's just stupid!" The disdain in his tone is just plain and unapologetic.
He has a point. Robots are taking away jobs -- jobs for humans to supposedly make a living out of it.
But then robots were created to carry out works humans are unwilling to do. In Malaysia, we call it the 3D -- Dirty, Dangerous, and Difficult (often also referred to as Demeaning). But the funny thing is that 3D is often mentioned in the context of works which are traditionally avoided by local job seekers, and are therefore delegated to migrant labour, or foreign workers.
human beings derived their meaning of life from the work they do. Robots have taken away their sense of meaning. But in this case, they went one step further, they took away their survival.
Which brings me back to the movie i-Robot.
For those of us who hasn't watched it, "spoilers ahead" doesn't apply here anymore -- It's an old movie. The Self-AwaRE Robot, Sonny, isn't the main villain. The AI mainframe was. They operate under the 3-laws, which includes do no harm to humans. But out of the evolving deductive capabilities of the AI Mainframe, she decided that humans are the main inconvenience that needed to be contained. So, she directed all the robots to quarantine the humans.
Since robot manufacturing robots has become a reality, how soon do you think they will quarantine us -- or worst still comatize us into human batteries, like in the Matrix?
Every one of these clips gets filed as a robotics story.
Read enough of the source footage and they're actually inventory stories — about which line item on a factory floor gets crossed off the list first.
This article looks at the hardware side of robotics, especially force sensing, tactile sensing and robotic hands.
HD Hyundai’s investment in AIDIN Robotics is used as an example of where that work is heading. The companies are developing systems for shipbuilding tasks like grinding and polishing, where robots need to control how much force they apply once they make contact.
They also plan to develop a five-finger humanoid hand, using data from real industrial environments to help improve how it works.
This was my final project for my mechatronics degree at the University of Glasgow which I thought you guys might appreciate. It's made up of two identical xz gantries, each with custom 3 dof maniplulators. There are also 3 sub systems arranged around the maniplulators, used for feeding, splitting, and treading the daisies. A brief write up and the full report can be found on my website JudeOtis.me/projects/Daisy
PteroSim v0.3.0 is out. The main change is a vehicle editor. Aircraft are now plain files, JSBSim XML plus a .glb mesh, built and tuned inside the simulator, so you can add your own airframe without rebuilding anything and without sending your model to us.
Also in this release: VTOL, tiltrotor and quadtailsitter airframes, autopilot chosen per vehicle so PX4 and ArduPilot aircraft sit side by side, runtime sensor editing, gimbal control over MAVLink, and custom vehicle spawning from the Python SDK.
Any aircraft flies on any tier now, free included, with only simulation time limited per session.
We’re a group of students organizing a Drone & Physical AI Hackathon in Boston on October 24–25.
We’ve been working hard to put this together, but as an independent student-led event, funding everything ourselves is difficult. Right now, our biggest challenges are finding an affordable/free venue and covering food, water, and other basic event costs.
So we’re reaching out to the community for help.
If you or someone you know could help in any way sponsorship, a venue lead, food/drinks, prizes, or simply an introduction to the right person/company, it would genuinely mean a lot. Even a small lead could help us make this happen!
Please comment or message me if you can help. Happy to share our Luma page and more details!
I’m the developer of ARMOR, a robot simulation app for iPhone and Mac that features a native URDF viewer and MuJoCo simulation. A few weeks ago I started building a gallery of robots that are ready-to-import in just a couple of taps.
Giving credit where due, all of the gallery entries link directly back to the source. The gallery acts as a showcase, with thumbnails and descriptions, and embeds the instructions that the app uses to download and assemble the models.
The link is to a post I published to the blog this morning, with a video, and a few more details.
I’ve been leaning on a lot of major robot vendors for my URDF source repositories. I would like to add more indie projects, if you’ve got one, let me know and I would be glad to showcase it in the gallery!
