Semantic-based Loco-Manipulation for Human-Robot Collaboration in Industrial Environments

Robots with a high level of autonomy are increasingly requested by smart industries. A way to reduce the workers’ stress and effort is to optimize the working environment by taking advantage of autonomous collaborative robots. A typical task for Human-Robot Collaboration (HRC) which improves the working setup in an industrial environment is the $\mathit{\text{"bring}}\mathit{\text{me}}\mathit{\text{an}}\mathit{\text{object}}\mathit{\text{please"}}$ where the user asks the collaborator to search for an object while he/she is focused on something else. As often happens, science fiction is ahead of the times, indeed, in the $\mathit{\text{Iron}}\mathit{\text{Man}}$ movie, the robot $\mathit{\text{Dum-E}}$ helps its creator, $\mathit{\text{Tony}}\mathit{\text{Stark}}$, to create its famous armours. The ability of the robot to comprehend the semantics of the environment and engage with it is valuable for the human execution of more intricate tasks. In this work, we reproduce this operation to enable a mobile robot with manipulation and grasping capabilities to leverage its geometric and semantic understanding of the environment for the execution of the $\mathit{\text{Bring}}\mathit{\text{Me}}$ action, thereby assisting a worker autonomously. Results are provided to validate the proposed workflow in a simulated environment populated with objects and people. This framework aims to take a step forward in assistive robotics autonomy for industries and domestic environments.