Introducing Q-learning for grasping objects
Training a robot arm end effector to pick up an oddly shaped object using the Q-learning RL technique involves several steps. Here’s a step-by-step explanation of the process:
- Define the state space and action space:
- Define the state space: This includes all the relevant information about the environment and the robot arm, such as the position and orientation of the object, the position and orientation of the end effector, and any other relevant sensor data
- Define the action space: These are the possible actions the robot arm can take, such as rotating the end effector, moving it in different directions, or adjusting its gripper
- Set up the Q-table: Create a Q-table that represents the state-action pairs and initialize it with random values. The Q-table will have a row for each state and a column for each action. As we test each position that the arm moves to, we will store the reward that was computed by the Q-learning equation...
