An underdeveloped capability in soft robotics is proprioceptive feedback control, where soft actuators can be sensed and controlled using only sensors on the robot's body. Additionally, soft actuators are often unable to support human-scale loads due to the extremely compliant materials in use. Developing both feedback control and the ability to actuate under large loads (e.g. 500 N) are key capacities required to move soft robotics into everyday applications. In this work, we independently demonstrate these key factors towards controlling and actuating human-scale loads: proprioceptive (embodied) feedback control of a soft, pneumatically-actuated origami robot; and actuation of these origami origami robots under a person's weight in an open-loop configuration. In both demonstrations, the actuators are controlled by internal fluidic pressure. Capacitive sensors patterned onto the robot provide position estimation and serve as input to a feedback controller. We demonstrate position control of a single actuator during stepped setpoints and sinusoidal trajectory following, with root mean square error (RMSE) below 4 mm. We also showcase the actuator's potential towards human-scale robotics as an "origami balance board" by joining three actuators into an open-loop controlled system with a platform that varies its height, roll, and pitch. This work contributes to the field of soft robotics by demonstrating closed-loop feedback position control without visual tracking as an input and lightweight, soft actuators that can support a person's weight. The project repository, including videos, CAD files, and ROS code, is available at https://parses-lab.github.io/kresling_control.

This chip could increase the processing power of your next Raspberry Pi-powered Machine Learning project.

Adafruit Industries has created a machine learning camera built with the Raspberry Pi that can identify objects extremely quickly and audibly tell you what it sees. The group has listed all the necessary parts you need to build the device at home. The camera is based on Adafruit's BrainCraft HAT add-on for the Raspberry Pi 4, and uses TensorFlow Lite object recognition software to be able to recognize what it is seeing. While interesting on its own, DIY Photography makes a solid point by explaining a more practical use case for photographers: You could connect a DSLR or mirrorless camera from its trigger port into the Pi's GPIO pins, or even use a USB connection with something like gPhoto, to have it shoot a photo or start recording video when it detects a specific thing enter the frame. A camera that is capable of recognizing what it is looking at could be used to only take a photo when a specific object, animal, or even a person comes into the frame.

Adafruit Industries has created a machine learning camera built with the Raspberry Pi that can identify objects extremely quickly and audibly tell you what it sees. The group has listed all the necessary parts you need to build the device at home. The camera is based on Adafruit's BrainCraft HAT add-on for the Raspberry Pi 4, and uses TensorFlow Lite object recognition software to be able to recognize what it is seeing. You could connect a DSLR or mirrorless camera from its trigger port into the Pi's GPIO pins, or even use a USB connection with something like gPhoto, to have it shoot a photo or start recording video when it detects a specific thing enter the frame. A camera that is capable of recognizing what it is looking at could be used to only take a photo when a specific object, animal, or even a person comes into the frame.

The graph represents a network of 6,120 Twitter users whose tweets in the requested range contained ""machine learning"", or who were replied to or mentioned in those tweets. The network was obtained from the NodeXL Graph Server on Sunday, 15 November 2020 at 01:02 UTC. The requested start date was Friday, 13 November 2020 at 01:01 UTC and the maximum number of days (going backward) was 14. The maximum number of tweets collected was 7,500. The tweets in the network were tweeted over the 1-day, 23-hour, 55-minute period from Wednesday, 11 November 2020 at 01:05 UTC to Friday, 13 November 2020 at 01:00 UTC.

This cool looking robot is made using a Raspberry Pi and 3D printed parts. In this project, a dedicated algorithm is made so that the robot can autonomously navigate the track as well as perform tasks such as line following, detecting an obstacle, grabbing and delivering an object. In addition, robustness is also considered it is because if robot navigate the pathway multiple times its performance will not affect. Each Friday is PiDay here at Adafruit! Be sure to check out our posts, tutorials and new Raspberry Pi related products.

The DIY 3D printing community has passion and dedication for making solid objects from digital models. Recently, we have noticed electronics projects integrated with 3D printed enclosures, brackets, and sculptures, so each Thursday we celebrate and highlight these bold pioneers! Have you considered building a 3D project around an Arduino or other microcontroller? How about printing a bracket to mount your Raspberry Pi to the back of your HD monitor? And don't forget the countless LED projects that are possible when you are modeling your projects in 3D!

Adafruit's Circuit Playground is jam-packed with LEDs, sensors, buttons, alligator clip pads and more. Build projects with Circuit Playground in a few minutes with the drag-and-drop MakeCode programming site, learn computer science using the CS Discoveries class on code.org, It has a powerful processor, 10 NeoPixels, mini speaker, InfraRed receive and transmit, two buttons, a switch, 14 alligator clip pads, and lots of sensors: capacitive touch, IR proximity, temperature, light, motion and sound. A whole wide world of electronics and coding is waiting for you, and it fits in the palm of your hand. Join 14,000 makers on Adafruit's Discord channels and be part of the community!

The idea behind the BrainCraft board (stand-alone, and Pi "hat") is that you'd be able to "craft brains" for Machine Learning on the EDGE, with Microcontrollers & Microcomputers. On ASK AN ENGINEER, our founder & engineer chatted with Pete Warden, the technical lead of the mobile, embedded TensorFlow Group on Google's Brain team about what would be ideal for a board like this. We've started to design a BrainCraft HAT for Raspberry Pi and other linux computers. It has a 240 240 TFT display for inference output, slot for Camera connector cable for imaging projects, a 5 way joystick and button for UI input, left and right microphones, stereo headphone, stereo speaker out, three RGB dotstar LEDs, two 3 pin STEMMA connectors on PWM pins so they can drive NeoPixels or servos, and grove/stemma/qwiic I2C port. This should let people build a wide range of audio/video AI projects while also allowing easy plug in of sensors and robotics!

Out of the box, in less than 5 minutes, demonstrate and use Machine Learning (video). If you want to skip ahead to the demo-only, click here or scrub to: 4 mins, 50 secs. The idea behind the BrainCraft board (stand-alone, and Pi "hat") is that you'd be able to "craft brains" for Machine Learning on the EDGE, with Microcontrollers & Microcomputers. On ASK AN ENGINEER, our founder & engineer chatted with Pete Warden, the technical lead of the mobile, embedded TensorFlow Group on Google's Brain team about what would be ideal for a board like this. We've started to design a BrainCraft HAT for Raspberry Pi and other linux computers.