The perils of technical videos is that they can become out of date pretty quickly. For the moteus getting started guide, it has amazingly held up pretty well, but enough has changed since 2021 that it is time for a new one. Now we can use acceleration and velocity limited commands instead of the deprecated stop position and get nicer 4k b-roll for all the intermissions. Everything in the old video, and in this new one, is applicable to both the moteus-r4.11 and the moteus-n1.

People over in the mjbots discord frequently ask about how to build reliable XT30 cables, so I made a short video describing how I go about it!

With new python based moteus tview and moteus_tool available, and the new mj5208 motor being included in all the moteus devkits, I figured it was time to update the moteus getting started video. Here it is!

Here’s the approximately annual giant video update:

If you’re interested in any of the topics in more detail, I’ve collected links to individual posts for each of the referenced items below.

Thanks for all your support in the last year!

Moteus

Announcement of moteus r4.3: Production moteus controllers are here!

I saw a recent Skyentific video and decided to have a try at it myself, check out the result:

This is part of a continuing series on updated diagnostic tools for the mjbots quad A1 robot.  Previous editions are in 1, 2, 3, 4, 5, 6, and 7.  Here I’ll be looking at one of the last pieces of the puzzle, synchronizing the video with the rest of the telemetry.

As mentioned previously, recording video of a robot running is an easy, cheap, and fast way to provide ground truth information on all of the sensors and actuators.  However, it is only truly useful if it can be accurately synchronized in time to the other telemetry streams for the robot.

In previous posts of this series, I covered some diagnostics improvements I’ve made to help work on more advanced gaits for the mjbots quad A1 (1, 2, 3, 4, 5, 6).  This post will cover the last major new piece of diagnostics I added to tplot2, 3d rendering of telemetry data.

3D rendering

While it should be obvious, I’ll give a little exposition.  tplot2 in its state prior to this could show a “tree view” of all data logged in numeric form.  It had a “plot view” which let you plot any single floating point scalar vs time.  As of recently, it could also render video associated with a given point in time in the log.  However, as anyone who has ever tried to debug a 3d dimensional software application, much less a 3d dimensional robot, can attest, debugging with scalar numbers and time plots is only productive for a very limited range of problems.

This is part of a continuing series on diagnostics tooling for the mjbots quad series of robots.  The previous editions can be found at 1, 2, 3, 4, and 5.  Here, I’ll cover the first extension I developed for tplot2 to make it more useful to diagnose dynamic locomotion issues.

Background

Diagnosing problems on robots is hard.  The data rates are high, sensing is imperfect, and there are many state variables to keep track of.  Keeping track of problems that are related to erroneous perception are doubly challenging.  Without a recording of the ground truth of an event, it can be hard to even know if the sensing was off, or if some other aspect was broken.  Fortunately, for things the size and scope of small dynamic quadrupeds, video recording provides a great way to keep a record of the ground truth state of the machine.  Relatively inexpensive equipment can record high resolution images at hundreds of frames a second documenting exactly where all the extremities of the robot were and what it was doing in time.

I made up a YouTube video showing the techniques I used when manufacturing the beta edition of the qdd100 servos TODO link.  Check it out: