  Robotic manipulation is currently undergoing a profound paradigm shift due to
the increasing needs for flexible manufacturing systems, and at the same time,
because of the advances in enabling technologies such as sensing, learning,
optimization, and hardware. This demands for robots that can observe and reason
about their workspace, and that are skillfull enough to complete various
assembly processes in weakly-structured settings. Moreover, it remains a great
challenge to enable operators for teaching robots on-site, while managing the
inherent complexity of perception, control, motion planning and reaction to
unexpected situations. Motivated by real-world industrial applications, this
paper demonstrates the potential of such a paradigm shift in robotics on the
industrial case of an e-Bike motor assembly. The paper presents a concept for
teaching and programming adaptive robots on-site and demonstrates their
potential for the named applications. The framework includes: (i) a method to
teach perception systems onsite in a self-supervised manner, (ii) a general
representation of object-centric motion skills and force-sensitive assembly
skills, both learned from demonstration, (iii) a sequencing approach that
exploits a human-designed plan to perform complex tasks, and (iv) a system
solution for adapting and optimizing skills online. The aforementioned
components are interfaced through a four-layer software architecture that makes
our framework a tangible industrial technology. To demonstrate the generality
of the proposed framework, we provide, in addition to the motivating e-Bike
motor assembly, a further case study on dense box packing for logistics
automation.
