Page Comparison

Week 1

In week 1 we met up on the following days:

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• Kiran: look into the libraries useful for extracting features from the environment that can be used for Personalisation and Reinforcement Learning. Features to be extracted are:

  • Age

  • Clothing

    • There were problems in installing the libraries tensorflow and keras because of the MacOS and python version in the system resulting in conflicts.

      • Error - zsh: illegal hardware instruction python

    • Ran into a problem when installing mediapipe, but was resolved:

      • Error - No matching distribution found for mediapipe mac

        • Solution : Step 1 - Install Rosetta 2 ( /usr/sbin/softwareupdate --install-rosetta)

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The goal for today was to integrate speech recognition into the python file for learning that Kiran made. The second goal was to integrate all modules together.

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We had some major setbacks today. While Dialogflow did work on Siddhant’s laptop last Friday, it suddenly stopped working today. We spent a lot of time troubleshooting Dialogflow and also doing research into other options. Some of the other options we researched were: ALSpeechRecognition, using the sensors (head, left hand, and right hand). The sensors seemed like a good backup, however, we couldn’t get information about when the sensors were touched via the Python script. Then we thought we might design a ‘detect sensor touched’ movement via Choregraphe, but this also failed.

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• G_t is the encoded feedback.

  • The feedback means either to:

    • increase duration

    • decrease duration

    • stop

  • If the feedback means to increase, G_t would be a positive value.

  • But since we want to increase the duration, we do not want any negative value while updating that would decrease the theta_mu

  • Therefore we check if :

    Image RemovedImage Added

(where A_t is the prior duration action, mu_theta(S_t) is the mean of the Age range), is positive or negative. If the difference is negative and the feedback means to increase, G_t would be a negative value(say -0.1) and if the feedback is meant to increase and if the difference is positive, G_t would be a positive value(say 0.1).

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Though the original idea could be achieved by ensuring proper time was devoted to the project, we had to inevitably cut down on the modules we would require to achieve it. That is why the attire detection was dropped. However, for future work it could be very interesting to implement attire detection in combination with age detection.

We experienced some issues with the learning part of the experiment. None of us really had any experience with reinforcement learning, and this posed to be quite a challenge. Also some major setbacks were endured throughout the course (dialogflow, object recognition).