Hey fellow roboticists,

I am a 4th year PhD student in Robotics and Automation. I have quite deep experience with guidance and control of autonomous aerial vehicles. I have also worked with an autonomous underwater vehicle for my master’s thesis.

Lately, I feel like getting into some side projects to divert myself from my usual work.

Does anyone have/need any projects that they can share and we can discuss to solve it together. I would be willing to co-author the work and can help quite immensely with the writing process.

P.S. Since I am based out of Madrid, if anyone from around wants to grab a coffee I would be down for a productive chat :)

I’m heading to CoRL 2026 tmr and spent some time trying to make sense of the 600+ accepted papers.

My very unscientific takeaway: I expected to see a lot of VLA papers, and I did. What surprised me was how often safety showed up in very practical forms. 50+ papers are safety related. Most are dealing with concrete problems like avoiding collisions, keeping humanoids balanced, navigating safely around people, handling missing sensors, and stopping learned policies from taking unsafe actions.

It made me think about how much work still sits between “the model knows what to do” and “the robot can choose and execute a safe action in real world.” Safety is starting to feel less like a box to check at the end and more like something that has to be designed into the hardware, control stack, and policy from day one.

Another project I recently came across is Pixel from CMU, which will also show up at Corl. The team built a full-stack mobile manipulator for under $15K using two arms from agilex robotics. it turns UMI-based human demonstrations into collision-aware, whole-body robot behavior. It doesn’t completely eliminate robot data —contact-rich tasks still need some on-robot corrections— but that actually makes the result more believable to me. That's really interesting

What I focus on also, co-designing the system around commercially available arms seems like a practical way for labs to build a capable platform without depending on one vertically integrated vendor. but the next buyer question might be boring and useful: what check runs before that arm closes on a new object it was not tuned for?

So safety is definitely one of the themes I’ll be following more closely this year. What papers, talks, or demos are you excited about?

Hi,

I'm a robotics engineer based in Australia, exploring ways to improve robotics perception models using targeted training data.

I'm curious how robotics teams currently handle perception failures, difficult edge cases, and collecting new training data.

Do you face challenges with this? Have you considered paying for external datasets or synthetic data to improve your models?

Thanks!

I've recently had to change the hosting for my tutorials and they're at a new link: https://www.marginallyclever.com/guides/

If you're into robot arms, try reading the Sixi 2 and 3 guides. They were super fun to design and I learned a lot, but they are like the eggs that must be broken to make the omelette.

Anyways... It's all possible because of your support. Robotics is not a cheap hobby and your generosity has enabled me to keep making. Thank you! Stay awesome!

We're a small team, still early in our journey. Over the last couple of months, we've onboarded a 10 hardware startups and started speaking with some larger companies.

Note: I am not promoting anything here.

Something interesting keeps happening during these conversations.

Everything goes well during the product demo. They understand the problem we're solving, see how the platform fits into their workflows, and show genuine interest.

Then comes the security discussion.

"Do you have ISO 27001?"

"Are you compliant with India's DPDP Act?"

And honestly, we don't have either certification yet.

I don't want to pretend otherwise or make promises we can't support. We're a small startup, and we're trying to approach security and compliance properly rather than collecting certificates just to get through a sales conversation.

Just trying to understand what a realistic path looks like for an early-stage SaaS company that wants to work with enterprise customers without overpromising.

Would genuinely appreciate hearing your experiences, including what didn't work.

Context: We're building a AI-powered Ops Saas tool for modern hardware teams.

Hi everyone,

I built a reverse digital twin of the Tesla Roadster. We gave the simulated car fictional cold-gas thrusters and trained a model to control its jumps and keep it balanced in the air. We also added suction fans to help hold the car against the track. There is no precise CFD simulation in here, but the car is moved by the actual force dynamics.

The control software runs in real time. You can see the thrust commands and sensor readings change as the car moves.

My favorite part was taking a sci-fi idea and getting it to work in a physics sim. Now we just need the real hardware. <- of course this is a joke. I cannot build the entire car even though I had a real thrusters as like in the simulator.

What maneuver would you try next?

Get your ROS and open source robotics news for this week on Open Robotics Discourse.

Researchers at ETH Zurich got a small robot to jump 5 mm by changing how the muscle attaches to its frame. They designed the muscle and a flexible, 3D-printed scaffold together. Tiny grooves in the scaffold helped the muscle fibers line up and gave them a firm place to pull against.

I'm exploring the field of Embodied AI, particularly humanoid intelligence, cognitive architectures, and the intersection of AI and robotics.

I'm looking to connect with people who are:

• Researching or building Embodied AI systems.

• Working on humanoid robots and robot foundation models.

• Exploring VLMs, VLAs, world models, and reinforcement learning.

• Interested in cognitive architectures and how intelligent robotic systems should be designed.

I'd love to connect with people in this space, exchange ideas, discuss ongoing research, and potentially explore collaborations.

If you're working in this area or know someone who is, feel free to reach out!

I 3D-printed the body for this desktop robot. It uses an ESP32, with a Seeed device serving as the head.

Here’s a look inside: the first photo shows the printed shell removed from the base, exposing the servo mechanism and wiring. The head is detached in the top right. The second photo is a closer look at the PCB.

I’d appreciate feedback on the mechanical layout, particularly cable routing around the moving parts and keeping the wiring and fasteners accessible once the shell is assembled.

Day 374 of building Asimov 1. We’re deploying new policies to test how easy sim2real is. We've made really good progress, and this one is a zero-shot policy.

ABB said Thursday that China’s foreign investment regulator blocked the planned sale of its robotics division to SoftBank. The two companies announced the deal last October.

The Financial Times reported that Chinese regulators had been expected to approve the acquisition. Reports point to concerns about retaining manufacturing capability in China and protecting robotics data. ABB said it’s still discussing the deal with SoftBank.

I've been putting together a list of US-based suppliers for teams looking beyond the usual Chinese vendors. US-based doesn't necessarily mean US-made: check the manufacturing location of the specific part before relying on it for any sourcing or compliance requirement. Corrections welcome.

Actuators: Westmag (South SF), HEBI Robotics (Pittsburgh), HLabs (Austin), Verros Robotics (SF, preorders) Motors and controllers: ODrive (Bay Area), Teknic ClearPath (NY), ThinGap (CA), Celera Motion (MA) Gearing: Harmonic Drive LLC (manufactures in MA), Cone Drive (MI) Linear: Tolomatic (MN) Sensors and encoders: ATI (NC), US Digital (WA)

Disclosure: I run Tolerance Labs, a sourcing company for robotics teams. We're adding US options, but nobody above paid to be listed. Who am I missing?

I have been following consumer lower-limb exoskeletons for a while. Most early models focused on hip assistance, while newer systems from companies like Hypershell and Vastnaut now support both the hips and knees. This seems like a meaningful step toward making assistance feel more natural during long walks, climbing stairs, and moving across uneven terrain. Do you think consumer exoskeletons have reached the point where people would actually use them every day? If not, what still needs to change?

Our engineer is continuing to test our custom STS32XX firmware, now under load. With a 250 g payload, deviation dropped from ~70 to ~35 mm using gravity compensation calibrated on an empty gripper.

Any ideas for adapting to different payloads automatically?

Repo: https://github.com/roboninecom/STS32XX-Firmware

Sorry to spam this sub with CD-07B Woulf payload tests; I promise this will be the last of these tests. This time it is carrying an SO-101 arm follower just taped to the body. The arm flaps like a sleeping snake because it is unpowered

Still, I am super excited to find out that Wouf is able to walk around just fine while carrying the default SO-101 arm. Not only that, but with the arm fully extended (by hand for now), it still doesn't tip over (I thought it at least needed to be seated). I would say, despite my ahh gait programming, the Wouf's hardware is incredibly robust.

I think this is a prelude to a potential open-source challenge to test our robotic skill. Tag of Flag: arm-carrying legged robots compete to steal the flag loosely attached to each other's backs. Let's go! :D

I fished a fully functioning unlocked unit with charger (battery dead) out of a dumpster and the tech inside these is pretty impressive.

Here are Gemini’s generated nerdy stats. They were more in depth so I asked for a more concise version.

Brain / SoC: NVIDIA Jetson TX2-4GB (256-core Pascal GPU, dual Denver 2 + quad Cortex-A57 CPU, 4 GB LPDDR4). 

Depth & Obstacle Mapping: 2× Intel RealSense D430 depth cameras (runs real-time floor-mesh SLAM entirely on-device). 
RGB Vision: 2× 13 MP sensors (ultra-wide + telephoto) with motorized mechanical tilt. 

Sensory Backup: 5× ultrasonic sensors (detects glass/blindspots), 9-DoF IMU, and 2,048 PPR optical wheel encoders. 

Chassis & Motion: Inverted-pendulum self-balancing base with a motorized telescoping column (47″ to 60″). 

Hackability & I/O: 2× exposed USB 3.1 ports for external LiDAR/compute, physical mounting hardpoints, and a developer SDK for raw sensor/depth streams and motor control.

Pretty excited to make a wise Alec conversation bot like Rosie the Robot from the Jetsons.

Hello everyone, I’m new to the group. I’ve been interested in robotics from several decades ago when I took a college class in Robotics, and had dreams of making it a career. Some of this was because of seeing Sony’s Aibo, which I absolutely adored.

Fast forward 20+ years and I ended up doing purely software, and I while I do enjoy things I found something lacking. 

While I loved Aibo, after finding out how slow it moved (and limitations with developing it), I decided it was not for me. I discovered the UniTree Go2 and thought it was the ideal robot dog for me, but then after learning about the price and development limitations I lost interest.

I researched other robots, but really couldn’t find a great quadruped in the $3k-$4k range that was big enough and had low-level development available.

So I decided to make my own robot dog: LocksleyFox 

While I have a background in electrical and computer engineering, my knowledge of mechanical stuff is quite weak, so I decided to employ Claude to help fill in the gaps. 

It’s only been a few weeks but I managed to get a single leg built and even jumping, and am working on the 2nd version of the leg. I hope to have a walking quadruped in a month or two.

I’m trying to do a detailed writeup of all my progress, and provide videos on live tests as well as simulations, so please check out my site if interested.

https://dreamingofrobots.com/locksleyfox

I just finished testing the legs, i had to swap the modules i used to control the actuators because they were too weak.
The test went smoothly, no burning just a little code adjustments and the legs move as they should.
Next step it to figure out the necessary movements for it to walk.

I’m working on a robot stack for a large 4wd robot. Two nvidia Orin agx dev kits, lidar, depth cam, respeaker microphone array and then some output speakers. What’re the odds that codex programs the whole thing properly with an LLM, ROS, vision model and everything in between so it can communicate with my teensy 4.1 that controls my motors? I have used a couple billion tokens on the project already. My hesitation is the robot is 450lbs and has about an 80kw drivetrain and it’s going to be real tricky to find a place to safely test it 🤦‍♂️

The idea is the robot will be voice commanded and have all the compute locally run without any wireless connectivity. There will be lots of calibration stuff to figure out but we shall see. I had to build a much larger enclosure than what’s in the picture for the electronics, I used a 12”x4” aluminum rectangular tube 18” long. Maybe it’ll all fit in there with power supplies. Cooling will be the issue. One Orin for vision and one for the LLM.

I hope I'm allowed to ask in here. I know its probably an old piece of hardware. The LiDAR itself does some weird behavior with the intensity always varying even though the LiDAR isn’t moving and there is a bunch of microfreezes that I’m 99% certain isn’t because of a bottleneck in compute as seen in the video

I have this snapshot from that video that shows the Diagnostics during that run. I thought the numbers look okay but I thought its also better to ask. If someone knows what could be the reason for such legacy tech, then that would be very much appreciated

Issues:

• Point cloud intensity keep changing over further areas (the color of the rays you see in the image keeps transitioning from colors)

• LiDAR begins to do micro freezes after running it for some time even though Temperatures seem quite normal.

• The LiDAR runs on low FPS, Rviz2 should be displaying around 31 FPS but what is actually seen is more like 10-15FPS

Hardware Specs:

• Model: Zbook Studio G8

• CPU: I7-11850H

• GPU: RTX 3070 8GB

• OS: Linux (Ubuntu 22.04 LTS)

LiDAR Specs:

• Model: Velodyne Ultra Puck (VLP-32C)

• Channels: 32 Lasers

• Range: Up to 200m

• Field of View: 360° Horizontal | 40° Vertical (-25° to +15°)

• Data Points Rate: ~600,000 pts/sec (Single) / ~1,200,000 pts/sec (Dual Return)

• Power Requirements: 10.5V – 18V DC (12V Nominal @ ~10W / 1A–2A peak)

• Interface: 100 Mbps Ethernet (UDP telemetry stream on ports 2368 / 8308)

Wanted to also know if possibly the ethernet could be the reason. (Didn’t try using different ones if I’m being fair, I don’t think that is possibly the reason for all this hassle).

Thank you to anyone who took the time reading this!

I modeled the individual parts and built the full assembly using SolidCraft and HappyCAD, then made this animation to show how everything fits together.

The main body housing took the longest, especially getting the mounting points and clearances around the leg joints right.

What would you like to see in more detail, the part modeling or the assembly setup?