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Recently, when I was watching the Terminator: Dark Fate (By the way, I was disappointed with the whole reboot kind of thing, for me, Judgement Day was the ultimate Terminator movie).
Anyways back to our discussion, the movie made me felt that filmmakers, writers, and even some journalists put the robots in a bd light like they are some virus out to kill us all.
The basic idea here is that Robots are here to help, and we are going to discuss a method to make them more efficient at that. So, let’s begin!
Machine learning is a part of AI and uses algorithms to train the machines to aggregate, analyze, and predict data patterns. There are three types of algorithm learning methods used in the Artificial Intelligence paradigm. They are.
- Supervised learning
- Unsupervised learning
- Reinforcement learning
Supervised learning is a human way of mentoring machines with data patterns. Unsupervised learning explores self-learning and allows the machine to learn by itself. Reinforcement learning is like placing a machine to play the game of life. They are trained to act in a specific environment, with pre-requisites to handle the situations.
Continuous Hybrid Controls in Robots:
- Continuous Actions- analog outputs, torques or velocities
- Discrete Actions- control modes, gear switching, or discrete valves.
A hybrid control merges the continuous and discrete actions for optimal endpoint function in robots. Using the same algorithmic model of reinforcement learning chooses between continuous and discretization of the actions during an industrial process more reliable.
While MPO explores a paradigm, where inference formulations are used. They start by distributing the data over trajectories and create a relatable outcome. Then, estimate an optimized distribution over the trajectories consistent with the results.
Execution of Hybrid MPO for Continuous-Hybrid Controls:
Every robotic action, whether continuous or discrete is controlled through programs written in machine language interpreted through a processor in the robotic system that converts the codes into mechanical energy through servo motors.
A hybrid policy integrates continuous and discrete actions to create asynchronous hybrid control. It provides the optimal reward for formulations. Let’s take an example of drilling a hole in the steel plate.
A robot needs to drill a hole of 0.75 mm into a high gauge steel plate. Now, there are two types of actions here. One is to create a forward push for the drill tool that comes through the continuous action of torque/velocity.
While the other is to switch gears to reach that modulated torque for the safety of the tool, which is a discrete action. Too much velocity can kill the tool by overheating.
So, the hybrid MPO executes a hybrid policy exposing multiple “modes” to the agent. So, the robot can select the correct policy with continuous and discrete action.
Robotics has been evolving for quite some time. The dream of industry 4.0 is already here, and we are seeing new advances in robotic automation. Here, I have tried to decipher the RL model and its application on robot controls. It is an amazing advancement into the automated industrial robotics and one which will help us create efficient processes.