• https://github.com/rhasspy/piper
• microsoft/onnxruntime#9371

• Object Detection
  • Add the class/box confidence value to the message
• Face descriptor
  • Add a value measuring the blur of the face to the message (std of laplacian filter output)
  • Add a flag indicating how the face is aligned to the message

https://arxiv.org/abs/1703.07737

• CAD

  • Fit the NVIDIA Jetson AGX Orin
  • Add a LED strip around the base (WS2812B, 30 LEDs / m)
  • Add a LEDs diffuser
  • Add buttons to control the sound level
  • Replace discrete LEDs by a strip (WS2812B, 144 LEDs / m)
  • Add a LED strip to show the sound level (WS2812B, 144 LEDs / m)
  • Add springs to the servo horns to reduce the power consumption
  • Add light sensor
  • Add tie wrap hole on the stewart bottom

• PCB - PSU Control

  • LEDs around the base connected to PCB Control
  • Replace the ISO7521C by a tranceiver (MAX3221E)
  • Button inputs
  • The MCU closes the power so it can send a signal to the computer (example)
  • K7805-3AR3 for 5V
  • Add a EMC compliance circuit
  • Add a light sensor to adjust the LED brightness (ALS-PT19)

• PCB - Replace the OpenCR with a custom board using a Teensy 4.0

  • Use a Schmitt trigger for the limit switch
  • UART for PSU Control (tranceiver MAX3221E)
  • Button inputs (74HC2G14GV)
  • TTL Dynamixel IC: SN74LVC2G241DCTR
  • TTL Dynamixel Connector: 25SH-C-03-TS
  • IMU (ICM-40627)
  • R-78E5.0-0.5 for 5V
  • OpenCR Schematic

• Firmware - psu_control

  • Use this lib for the LED controls
  • Read the buttons to change the sound level
  • Send the current volume to the OpenCR
  • Read LED animation from the OpenCR
  • Max LED brightness 50% max (adjusted with the light sensors)
  • Max audio power: 12 W (battery) or 30 W (PSU)

• Firmware OpenCR

  • Send the volume to ROS
  • Read the LED animation from ROS
  • Send the buttons to ROS
  • Remove ROS and use a custom serial protocol

• Jetson AGX Orin

  • Create a 40W power model aligned with the software when the robot is powered by the battery (Power Estimator)
  • Create a 55W power model aligned with the software when the robot is powered by the power adapter (Power Estimator)
  • RTC Clock battery
  • Create a systemmd service (https://github.com/vasilakisfil/hello.service/blob/master/debian/hello.service, https://fabianlee.org/2017/05/21/golang-running-a-go-binary-as-a-systemd-service-on-ubuntu-16-04/, https://www.freedesktop.org/software/systemd/man/daemon.html) to:
    • Listen shutdown events
    • Change the power mode depending on the powering device (PSU or battery)
    • Listen and send data to ROS
    • Close all roslaunch processes
    • Adjust screen brightness (light sensor)
    • System Tray Icon
      • State of charge
      • Volume up
      • Volume down
      • Close all LEDs
      • Reset torso
      • Reset head

• ROS

  • Update the stewart platform parameters
  • Update the odas configuration
  • Update TF
  • control_panel
    • read the volume from opencr

• Documentation

  • BOM
    • PSU Control PCB
    • Buck-Boost PCB
    • Dynamixel Control PCB
    • 3D printing
    • Laser/CNC
    • Others
  • Assembly
  • TF
  • RS232/UART protocol
  • Electrical block diagram
  • Stewart displacement (root readme)

• Update main readme images

Ideas

• Use only one MCU

https://github.com/NVIDIA/Torch-TensorRT

• Robot state
  • Add the activation of the strategies
• Video analysis
  • Add the class/box confidence of the object
  • Add the blur measure of a face
  • Add how the face is aligned
• Audio Analysis
  • Add ODAS tracking
• Add odas to head TF

• Add the ODAS tracking in the message
• Reduce the interval between analysis
• ODAS frame Z at the base

https://github.com/pytorch/hydra-torch

https://github.com/openai/whisper

• Use AdamW
• Use knowledge distillation
• Object detection
  • Use pretrained yolov7 and yolov7tiny
  • Use yolov7 and yolov7tiny for object descriptor
• Person pose
  • Use efficient net instead of resnet18
  • Optimize the gaussian parameters
  • Increase the resolution of the output
  • BCE divergence for knowledge distillation
  • Training
• Face recognition
  • Improve the face alignment, maybe just a crop
  • Use efficient net instead of OpenFace
  • Use ArcFace instead AM-Softmax (https://openaccess.thecvf.com/content_CVPR_2019/papers/Deng_ArcFace_Additive_Angular_Margin_Loss_for_Deep_Face_Recognition_CVPR_2019_paper.pdf)
  • https://odr.chalmers.se/server/api/core/bitstreams/0f358a79-f2e1-48f3-b591-a4563489cd8f/content section 3.3 for knowledge distillation
  • Test also on a modern dataset like
  • Training
• Audio classification
  • Use AudioSet
  • Use a smaller frame size
  • Use https://arxiv.org/pdf/2102.01243v3.pdf
  • Training
• Speaker identification
  • Reduce the frame duration to 1 sec
  • Use the full size network
  • Use the pretrained network from speechbrain
  • Training