This paper presents the application of a statistical framework that allows to endow a humanoid robot with the ability to perform a collaborative manipulation task with a human operator. We investigate to what extent the dynamics of the motion and the haptic communication process that takes place during physical collaborative tasks can be encapsulated by the probabilistic model. This framework encodes the dataset in a Gaussian Mixture Model, which components represent the local correlations across the variables that characterize the task. A set of demonstrations is performed using a bilateral coupling teleoperation setup; then the statistical model is trained in a pure follower/leader role distribution mode between the human and robot alternatively. The task is reproduced using Gaussian Mixture Regression. We present the probabilistic model and the experimental results obtained on the humanoid platform HRP-2; preliminary results assess our theory on switching behavior modes in dyad collaborative tasks: when reproduced with users which were not instructed to behave in either a follower or a leader mode, the robot switched automatically between the learned leader and follower behaviors.
1 REF proposed to use Gaussian mixture models (GMM) and Gaussian mixture regression (GMR) to, respectively, encode and reproduce robot collaborative behaviors.
Teaching physical collaborative tasks: object-lifting case study with a humanoid
2009 9th IEEE-RAS International Conference on Humanoid Robots
2009 9th IEEE-RAS International Conference on Humanoid Robots
Computer Science