HandGuidance of a Mobile Manipulator Using Online Effective Mass Optimization This work addresses the field of humanrobot collaboration, in particular, the handguidance of a redundant mobile manipulator. An efficient handguidance is related to the improvement of the inertial properties of the endeffector felt by the operator, as these describe the effort to guide the robot. In this regard, the operational space formulation is used to minimize the effective mass in the null space of the handguidance task. The minimization is based on a geometric ellipsoid representation and applies a gradient descent method. The approach is tested on the KUKA VALERI mobile manipulator and implemented on a realtime framework, whereby a particular focus is on stability and safety. For this purpose, a power limitation is proposed. Finally, the improvement of the effective mass related to a specific direction is discussed.

Fast Manipulability Maximization Using ContinuousTime Trajectory optimization A significant challenge in manipulation motion planning is to ensure agility in the face of unpredictable changes during task execution. This requires the identification and possible modification of suitable jointspace trajectories, since the joint velocities required to achieve a specific endeffector motion vary with manipulator configuration. For a given manipulator configuration, the joint spacetotask space velocity mapping is characterized by a quantity known as the manipulability index. In contrast to previous controlbased approaches, we examine the maximization of manipulability during planning as a way of achieving adaptable and safe joint spacetotask space motion mappings in various scenarios. By representing the manipulator trajectory as a continuoustime Gaussian process (GP), we are able to leverage recent advances in trajectory optimization to maximize the manipulability index during trajectory generation. Moreover, the sparsity of our chosen representation reduces the typically large computational cost associated with maximizing manipulability when additional constraints exist. Results from simulation studies and experiments with a real manipulator demonstrate increases in manipulability, while maintaining smooth trajectories with more dexterous (and therefore more agile) arm configurations.