Before moving forward on with this project, I felt that it is important to explain its scope since some aspects might not be immediately clear, especially if you're not familiar with the details. Let's make sure we're on the same page.
Simply put, this project aims to make robots learn and adapt naturally, much like animals do. Instead of being explicitly programmed, they'll learn from real-world experiences. Picture robots navigating homes and offices, understanding the environment through touch, vision, and balance, just like infants do.
What's unique? These robots won't follow preset instructions; they'll intelligently respond to unfolding scenarios. Think of them assessing situations and making decisions on the fly, blending internal states with external stimuli.
And here's the kicker: the knowledge they gain won't stay confined to a single robot. Through a shared cloud knowledge base, robots will pass on their learned skills, accelerating collective learning.
This means that anyone can train a robot, and someone else across the globe can use those learned skills for their robot.
We're not replicating humanoid robots; the goal is versatile automatons that think smartly and act practically. This overview isn't rigid; it's just what we're aiming for - an exciting journey into a future of adaptable, useful robots.
Key Scope Elements:
Learn from Physical Interactions - Robots operate in home and office spaces, constructing knowledge in an unstructured fashion through movement and object interactions, much like infants. Touch, vision, balance - varied senses feed expanded neural models.
Respond Intelligently - Rather than just pre-mapped behaviors, the robots assess scenarios as they unfold to determine appropriate activities. Goal-directed yet reactive actions couple internal state with external stimuli.
Transfer Knowledge - Learned skills can transfer across robots through a cloud knowledge base, allowing skills to build rapidly by sharing rather than isolating experiences.
Critical Associations - Correlating occurrences in time and space provides environmental insight - when doors open, movements cause loud noises, certain objects appear together. Causal inference distills wisdom.