Dactyl The Robot: A Step Towards Artificial General Intelligence
By: Ashley C
Tags: Robotics, Rubiks Cube, OpenAI, Artificial Intelligence, Reinforcement Learning

OpenAI has reached a new milestone in the field of self-learning robots. Dactyl, the humanoid robotic hand (yes the hand has a name) has learned to solve a Rubik’s Cube, one-handed. The company sees this accomplishment as a step forward to developing the capabilities of its software, but what makes Dactyl so special?

Learning Virtually

For most people, picking up an object and analyzing it is a simple task, but do not tell a machine that! In fact teaching a computer to detect objects, pick them up, and examine them is more complicated than you think. Something that may take a human a few days to figure out, can take machines weeks of learning. So if humans can learn how to solve Rubik’s Cubes way faster than a machine can, as well as completing the puzzle much faster than Dactyl, what makes this Dactyl so special? Dactyl can learn new tasks using virtual simulations before it is presented with a real situation physical challenge to overcome. This means that as OpenAI develops this specific software, a robot will be able to perform a variety of real-world tasks, without having to train for months to years of real-world time and without needing to be explicitly programmed.

Artificial Intelligence Vs Artificial General Intelligence

Open A.I.’s software is not just aiming for regular artificial intelligence, but rather something called artificial general intelligence. Essentially regular artificial intelligence trains a model to execute one specific task at the highest performance possible, but if the model is asked to execute another separate task, it will not be able to execute it at a high-performance standard (since it is not trained for that task). In contrast, artificial general intelligence focuses on a more broad aspect, having a goal for the model to be able to think and function similar to humans, meaning that the A.I. will have its own decision-making capabilities for different scenarios (regardless if it is trained for that task).

A General Purpose Robot

Plenty of robots are able to solve a Rubik’s Cube faster than a human since they are specifically programmed to carry out a categorical task. Although, OpenAI is not trying to build a specialized robot, but rather a general-purpose robot. Similar to how humans can do many tasks, not just a specific thing, Open AI is developing software that is similar to the human scope, in terms of capabilities.

Open AI chose not to program individual motions for Dactyl to perform since the goal was for the hand to learn the movements on its own. Instead, Dactyl’s software was given a goal of solving a mixed-up Rubik Cube and used reinforcement learning to help Dactyl figure out how to solve the cube on its own.

How Does Reinforcement Learning Work?

Reinforcement learning allows for a model to learn through the consequences of actions that take place in a specific environment. This type of learning is a behavioural model, meaning that the algorithm provides data analysis feedback, which the agent will use to improve its results. In contrast to other forms of supervised learning, reinforcement learning is different because the data set does not train the machine. Instead, the machine learns by trial and error, meaning that a series of correct decisions essentially strengthen the model to better solve the goals in the future.

Similarly, humans experience a form of reinforcement learning when we are children. For example, when young children first learn how to walk, they will fall, but they have their parents there to lift them up and teach them. The more they practice, the easier walking gets, and this analogy corresponds to how machines undergo reinforcement learning.

In essence, when a machine undergoes reinforcement learning, it goes through teaching based on experience in the manner in which the machine must deal with what went wrong and look for the right approach. We do not give the model any tips or advice in regards to how to solve the game. It is up to the model to learn how to execute the task so it can optimize the reward. This process will begin with random testing and sophisticated tactics.