Aprendizaje de movimientos en un robot humanoide por medio de imitación

Abstract

The degree project titled Learning movements in a humanoid robot through imitation implements supervised learning algorithms, providing a robot with the ability to identify movements made in the upper limbs by a person and replicate them. The quantitative performance of the implemented architectures is also evaluated. To develop movement learning, a robotic platform called Poppy Torso is selected, corresponding to an open source project. This robot is built by means of 3D printing of the parts and their subsequent assembly. This development also contains the virtual description of the robot, which allows simulation in tools such as Rviz. The development of the project is divided into two stages: the first of them consists of achieving the classification of human movements. For this reason, an RGB-D (RGB plus depth) video dataset is created, containing movements of the upper limbs, performed by a group of people with different characteristics. Using a pose extractor model, the spatial location of the joints is extracted, which serves as input to a recurrent neural network (RNN-many to one) and an SVM (Support Vector Machine). The performance of these algorithms is evaluated by means of confusion matrices. The second stage consists of a regression problem that is based on replicating the execution of the movement in the robot. Due to this, a dataset is created that contains the trajectories of angles that each motor takes, so that the robot moves its joints from a random point to a defined one. For supervised learning, a recurrent neural network (RNN-one to many) is implemented, capable of predicting the trajectories of each of the robot's joints to reach a target point. The classification algorithms are evaluated by means of confusion matrices, where accuracies of 97 \% are obtained for the SVM and 92% for the RNN. On the other hand, the regression algorithm is evaluated with final position error, where errors between 9.32 x 10 ^ (- 8) and 4.21x 10 ^ (- 3) radians are obtained with respect to the target position. Due to the value of these metrics and the behavior of the algorithms when predicting, it can be concluded that the learning in the classification and regression algorithms develops adequately, fulfilling the proposed objectives.