BOBO’s Features

BOBO’s 3 main structures

Physical Capabilities, Cognitive Abilities, Ed-Tech Applications.

These structures have unlimited development possibilities and every development undertaken within these structures will introduce a new version and unlock fresh possibilities

physical_abilities
cognitive_abilities
ed_tech_abilities
BOBO’S AGENTS
visual_processing

Visual Agent

AI-based analysis of camera-captured images for tasks like object detection, recognition, and tracking, enabling real-time interpretation and adaptive decision-making

environmental_processing

Environmental Agent

AI-based analysis of sensory data from the surrounding environment, including lidar, distance sensors, cameras and other environmental factors, enabling systems to monitor, interpret, and respond to changes in real-time

Voice_processing

Voice Agent

AI-based analysis of audio input to perform tasks such as speech recognition, sound detection, and voice commands interpretation, enabling systems to understand and respond to verbal interactions in real-time

contextual_processing

Contextual Agent

Involves the use of large language models (LLMs) trained across multiple layers, running on our own servers, to analyze and interpret complex contextual data

audio_processing

Audio Agent

AI-based analysis of audio input to perform tasks such as speech recognition, sound detection, and voice commands interpretation, enabling systems to understand and respond to verbal interactions in real-time

movement_processing

Movement Agent

Involves the use of large language models (LLMs) trained across multiple layers, running on our own servers, to analyze and interpret complex contextual data

video_processing

Voice Agent

Displaying appropriate facial expressions and visual data on the head and body-mounted screens, effectively communicating the system’s response to the user through visual cues

emotion_processing

Emotional Agent

Analyzing the emotional states of both the system and the user through touch sensors and visual processing, enabling the system to generate appropriate responses based on emotional feedback

learning_processing

Learning Agent

eneral analysis of all inputs and outputs, with the implementation of relevant models to adapt and refine the system’s behavior, enhancing its ability to learn from interactions and gain subjectivity over time

educational_processing

Educational Content Agent

Processing selected curriculum materials according to the user’s preferred language and age, and adapting educational experiences to individual learning needs and implementing them interactively with the user

SUPPORT THE PROJECT

We are grateful for the support of our partners who believe in our vision and help bring this project to life