Robotic Approach Trajectory Using Reinforcement Learning with Dual Quaternions

Manipulation tasks in robotics usually involve two phases: an approach to the object and the grasp itself. The first action allows the robot to reach a certain pose in space that is likely to allow the object to be manipulated. Reinforcement Learning (RL) techniques allow a policy to be learned through experience given a set of states and actions, so this is a powerful tool for developing controllers for specific tasks such as positioning the robot in a particular point in space. However, when manipulating an object, orientation is as relevant as position. For this reason, a method of RL for positioning the robot's end effector in a suitable position and orientation for manipulation in simulation is presented. This approach models the problem of computing the distance for the reward function using dual quaternions parameterisation, an element that can represent the pose and attitude of a rigid body in Euclidean space in a compact way without having to apply any constraints.