Author: Simon Birrell

Adding an SD Card to your Deep Learning Robot

The Deep Learning Robot comes with 16Gb built-in flash on the Jetson TK1 board. That’s fine to begin with, but after downloading a few Caffe models, you’ll be out of space. Fortunately, the TK1 comes with an SD Card slot for adding extra storage. This post describes how to add and configure an SD Card to give yourself more room.

SD Card on the Deep Learning Robot
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Docking and Recharging the Deep Learning Robot

The Kobuki charger costs an extra $49 when you buy the Deep Learning Robot and is well worth throwing into the package. With a few simple commands you can get your robot to dock and recharge itself, providing it is in the general vicinity of the charging station. The following is adapted from the Kobuki / ROS tutorials.

Here’s a video of docking place in my crowded living room. Please forgive the baby noises:

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A Deep Learning Robot that recognises objects

Saturday night at home. The Deep Learning Robot “Dalí” now trundles round the house identifying objects and saying their names, with variable success. In the video you can hear it identify correctly “cradle”, “studio couch”, “lampshade” and “home theatre” (it’s an American neural network). However there’s a surreal moment when it sees a non-existent “grand piano”.

The object recognition is with Caffe as described previously, with a few new ROS nodes to do the speech. More details as soon as I have a proper fix for the Bluetooth Speaker pairing issue.

The Art of Seeing: Integrating ROS, Caffe, OpenCV on the Autonomous Deep Learning Robot

recognising a washing machine

The frustrating thing about robotics is the amount of time you have to spend on problems that aren’t really to do with robotics at all. The Autonomous Deep Learning Robot comes with OpenCV4Tegra, a specially accelerated version of the OpenCV vision library. It comes with Caffe, a neural network tool that allows you to do pretty good object recognition. It also comes with Robot Operating System (ROS).

So how hard should it be to make these work together and get your robot to recognise the objects that it sees?

HARD!

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