Adaptive evolution strategy for robotic manipulation

Teaching a mobile robot to complete a task and to reproduce it is possible, but as the robot tries to replicate actions natural events as a wheel-slide would feed in inaccuracies on the localization of the robot mobile base, and it may be difficult to succeed replicating. Robot tasks can be represented as trajectories compound by a series of poses and movements. We propose an algorithm for adapting manipulation trajectories to different initial conditions from those of the learned assignment. The adaptation is achieve by optimizing in position, orientation and energy conservation. Manipulation paths generated can achieve optimal performance sometimes even improving original path smoothness. The approach is builded over the basis of Evolution Strategies(ES), and only uses forward kinematics permitting to avoid all the inconveniences that Inverse kinematics imply as well as convergence problems in singular kinematic configurations. Experimental results are presented to verify the algorithm.