Adaptive swept volumes generation for humanrobot coexistence using Gaussian Processes Letting humans and robots share a common space for collaboration is considered a consolidated practice. The trajectories followed by the robot must be safe for the human mate, especially when the robot holds dangerous tools or parts. At the same time, the productivity must be preserved, without imposing too restrictive limitations on the robotxe2x80x99s movements. This article proposes the use of Gaussian Processes to predict the motion of an operator in a robotic cell, with the aim of controlling the robot speed and avoid collisions. An adaptive approach is proposed and the model for the human motion is persistently reupdated. The resulting approach will be demonstrated to be less conservative than previous ones, while at the same time to preserve the safety of the operator. Real experiments have been conducted on the 7 d.o.f. ABB YuMi robot.

Cascaded Gaussian Processes for Dataefficient Robot Dynamics Learning Motivated by the recursive NewtonEuler formulation, we propose a novel cascaded Gaussian process learning framework for the inverse dynamics of robot manipulators. This approach leads to a significant dimensionality reduction which in turn results in better learning and data efficiency. We explore two formulations for the cascading: the inward and outward, both along the manipulator chain topology. The learned modeling is tested in conjunction with the classical inverse dynamics model (semiparametric) and on its own (nonparametric) in the context of feedforward control of the arm. Experimental results are obtained with Jaco 2 sixDOF and SARCOS sevenDOF manipulators for randomly defined sinusoidal motions of the joints in order to evaluate the performance of cascading against the standard GP learning. In addition, experiments are conducted using Jaco 2 on a task emulating a pouring maneuver. Results indicate a consistent improvement in learning speed with the inward cascaded GP model and an overall improvement in data efficiency and generalization.