From healthcare to retail, passing by the high-tech industry, mobile robots will move in and manipulate their environment. However, how can we create robots, particularly mobile manipulators that can work alongside humans in highly dynamic and unpredictable environments? The problem is that right now, mobile manipulators are simply not ready for such a deployment. It is not possible to program a robot for everything that might happen and it is very hard to perform a variety of complex tasks while still maintaining a high degree of reliability. We develop novel algorithms for improved reliability in human-shared environments, based on optimization fabrics, learning and sampling-based model-predictive control.