Building a robotics research setup that lives next to my desk (dfdxlabs.com)
Quick framing, since the post is long: I did robotic manipulation research at OpenAI from 2017–2020, and the tabletop setup back then cost roughly 10x this one and took a team to run. This project is me testing whether a single person can now do meaningful work on the same class of problems: starting with physical and software setup.
A few decisions I'm least settled on, and would love some pushback/feedback on:
- single arm vs. bimanual (I went single for cost/space, knowing it rules out things like folding cloth)
- not calibrating camera extrinsics/intrinsics for now
- RGB vs. RGB-D for from-scratch policies (ACT / Diffusion Policy)
And one I'm more confident about but expect disagreement on: not building on ROS 2 / LeRobot, and writing my own stack instead. Happy to get into the reasoning.
24 comments
[ 2.9 ms ] story [ 39.3 ms ] thread- I've heard the advantage of ROS besides the architecture is the ecosystem (driver integrations, etc). Is that not an issue because the arm supports a Python SDK OOTB?
- Any issues you've been running into with this setup?
- How do you determine if a session recording is good enough for training? Is 50/100 samples really all you need?
Would like to know your reasoning on not going with LeRobot.
>I do not intend to calibrate the camera’s extrinsics or intrinsics for now.
Sensible choice, although I suggest it's good in the long run to do at early stage in your setup, especially if you intend to collect data for policy learning.
Debugging trained policies for visual manipulation task can be a headache and having as much context on variables that can change is a good practice.
My previous setup was in Japan, a earthquake prone place and I wasted some time after realizing the camera got misaligned due to earthquake. A simple solution is just to place an Aruco marker on the table that tracks the relative extrinsic position of camera, and add it as metadata to collected teleoperation dataset.
I am not an official supporter of the library but am asking out of curiosity.
Something I’m working on is a hardware CLI for agents to run experiments, with a “CICD” pipeline that validates everything and means I can delegate more of the experiments to the agents. I wonder if you have any thoughts on this?
The idea is to allow the coding agent to run the full loop of experiments and validations, with vision, audio, button pressing, speaking etc to interact in place of the human
¹https://blog.comma.ai/mlsim/
Reminds me of https://rodneybrooks.com/why-todays-humanoids-wont-learn-dex... which is basically a stark warning against the hype.
My project is https://github.com/colinator/Ariel - basically, no VLAs - instead, "just write code". Or have the agents do it.
I don't have a writeup yet about applying Ariel to _this_ robot, but this is for a previous one: https://colinator.github.io/Ariel/post1.html.
Excited to follow your progress!
Tell me more! I am slightly biased in that direction. But can’t fully justify it at this point.
- Calibration is not required for VLA models.
- RGB or Stereo RGB inputs are sufficient for ACT, DP, and PI0/PI05.
- ROS2 is not strictly required, but it can be useful for sharing/co-developing codes. For instance, the Stanford team built a custom framework for diffusion policy instead. I also developed similar framework because ROS2 is not optimized for bi-manual manipulation or VLA workloads.
I wanted to build something making bows automatically and damn, pretty complex and expensive to buy the parts.
I was like, I can probably just buy a 3D printer, print all the parts and buy some motors, but it seems it's way more complicated than that.
Like to play with a single hand robot. It looks like you need 10k+$. I wanted to spend max 1k, 3D print parts, buy motorized parts on Alibaba, and code on my Mac + spare GPUs I have access to. I'll have to save a little :(
There should exist a minimal, clear, robotics library like what you’ve built. The Flask of the robotics world.