Learning Trajectory Distributions for Assisted Teleoperation and Path Planning.

Several approaches have been proposed to assist humans in co-manipulation and teleoperation tasks given demonstrated trajectories. However, these approaches are not applicable when the demonstrations are suboptimal or when the generalization capabilities of the learned models cannot cope with the changes in the environment. Nevertheless, in real co-manipulation and teleoperation tasks, the original demonstrations will often be suboptimal and a learning system must be able to cope with new situations.

A Haptic Shared-Control Architecture for Guided Multi-Target Robotic Grasping.

Although robotic telemanipulation has always been a key technology for the nuclear industry, little advancement has been seen over the last decades. Despite complex remote handling requirements, simple mechanically linked master-slave manipulators still dominate the field. Nonetheless, there is a pressing need for more effective robotic solutions able to significantly speed up the decommissioning of legacy radioactive waste.