Currently industrial robots perform rigidly programmed tasks in highly-constrained settings. Any change in product or process requires costly restructuring of hardware and software. ReconCycle will address these issues by introducing the concept of robotic self-reconfiguration in the largely unconstrained domain of electronic waste recycling, which is still dominated by manual labor. Automation in this sector can benefit from the fact that very large batches of the same device type are to be processed but with some model differences and showing different states of damage. To be able to deal with each of these individual models, the robotic system requires flexible adaptation. Thus, the scientific objective of ReconCycle is to introduce in this sector self-reconfigurable hardware and software based on a reconfigurable robotic cell developed in a previous project. A two-step procedure is foreseen: When changing from one device-type to another, reconfiguration shall be performed in an interactive mode, where the application-engineer will be able to provide input. But, when changing from one device-model to another within a given device-type, the cell shall perform re-configuration on its own through a combination of sensorimotor learning approaches and other AI techniques. This constitutes the main novel scientific contributions of ReconCycle and is aimed at advancing our understanding of robotic perception-action processes in unconstrained industrial settings. We will use highly compliant soft robots and end-effectors, allowing humans to operate together with the machines to complete any missing steps. This reduces automation complexity and brings this project into a feasible regime for up to TRL 6. Electronic waste recycling is an important and strongly growing economic and environmental sector. The industrial objective of ReconCycle is to introduce here a much-increased level of automation resulting in a potentially high long-term impact on industry and society.