Grounding the lexicon for human-robot interaction during the manipulation of irregular objects

Traditionally, manufacturing systems that employ industrial robots restrict the workspace of the robot with the objective of avoiding collisions with humans or other obstacles during operations. A recent trend in manufacturing is to design even more flexible manufacturing systems to adapt quickly to market demand. This manufacturing tendency leads to a system in which humans and robots collaborate, sharing the tasks and workspace. This type of hybrid manufacturing combines human and robot abilities and is based on using the advantages of each participant, focusing on the tasks each element is superior at. A human being, for instance, has superior sensorimotor skills to those of the robot in complex manipulation tasks, but does not possess large force capabilities. On the other hand, the robot presents superior abilities in repetitive tasks, like precision and repetitiveness. In this paper, a manufacturing framework to manipulate irregular products between a human operator and a robot is presented. The objective is to allow the robot and the human to interact in a common task sharing the same workspace, eliminating traditional physical barriers. The development of this collaborative module involves the design of strategies for grasping and kitting operations of irregular products that also includes the recognition of those products. The experimental platform is composed by a 12-camera motion capture system, KUKA KR60 industrial robot, which is equipped with an end-effector with deformable fingers and an RGBD sensor (depth sensing devices that work in association with an RGB camera). A lexicon based on some human gestures has been designed and tested on-line during human-robot interaction tasks. Results showed the usefulness of the method to be implemented with real industrial robots and further work has been envisaged related to security issues that have to be met during its deployment in other similar applications.