This machine learning study investigates a lowcost edge device integrated with an embedded system having computer vision and resulting in an improved performance in inferencing time and precision of object detection and classification. A primary aim of this study focused on reducing inferencing time and low-power consumption and to enable an embedded device of a competition-ready autonomous humanoid robot and to support real-time object recognition, scene understanding, visual navigation, motion planning, and autonomous navigation of the robot. This study compares processors for inferencing time performance between a central processing unit (CPU), a graphical processing unit (GPU), and a tensor processing unit (TPU). CPUs, GPUs, and TPUs are all processors that can be used for machine learning tasks. Related to the aim of supporting an autonomous humanoid robot, there was an additional effort to observe whether or not there was a significant difference in using a camera having monocular vision versus stereo vision capability. TPU inference time results for this study reflect a 25% reduction in time over the GPU, and a whopping 87.5% reduction in inference time compared to the CPU. Much information in this paper is contributed to the final selection of Google's Coral brand, Edge TPU device. The Arduino Nano 33 BLE Sense Tiny ML Kit was also considered for comparison but due to initial incompatibilities and in the interest of time to complete this study, a decision was made to review the kit in a future experiment.

Over the course of 2022, we had the pleasure of finding out more about a whole range of AI topics from researchers around the world. Here, we highlight some of our favourite interviews from the past 12 months. Rose Nakasi and her colleagues have developed a machine-learning method to detect malaria parasites in blood samples. We spoke to Rose about the motivation for this project, the progress so far, and what they are planning next. Paula Arguello, Jhon Lopez, Carlos Hinojosa and Henry Arguello won the best paper award at the International Conference on Image Processing (ICIP) this year, for their work "Optics lens design for privacy-preserving scene captioning".

RoboCup is an initiative to promote research in robotics through standardized competition and cooperation. The RoboCup Humanoid League focuses on legged robots between 0.4–1 metres tall in the KidSize and 1–2 metres tall in AdultSize. This year the Hamburg Bit-Bots performed a survey of all KidSize teams participating in the RoboCup 2022 in Bangkok, Thailand. We aimed to capture the state of the art in the league. This article gives a summary of the results.

RoboCup is an international scientific initiative with the goal of advancing the state of the art of intelligent robots, AI and automation. The annual RoboCup event returned to an in-person format for 2022, taking place from 13-17 July in Bangkok. RoboCup comprises a number of leagues, with perhaps the most well-known being the soccer leagues. In the Humanoid League, autonomous robots with a human-inspired body plan and senses play soccer against each other. We spoke to Jasper Güldenstein, a member of the technical committee, about the competition at RoboCup 2022, and also about the Humanoid League Virtual Season.