Optimising mechanical, control and actuator design variables together as a co-design problem enables identifying novel and better-performing robot architectures. Typically, solving such problems using conventional optimisation methods yields a single, point-based solution. Deviating from the computed optima may be necessary to ensure physical feasibility, typically associated with a performance loss. In this work, we present a two-step cascaded optimisation approach to identify non-intuitive designs and recover the loss in performance by constructing a solution space. The solution space provides robustness in the form of permissible ranges of design variable values and enables the selection of a physically feasible design. In our study, we observe (1) up to 20% of the lost performance is recovered and (2) an improvement of 30 % on the task metric in comparison to an existing robot and (3) designs with cost savings of up to 10% can be identified.

In recent years the robotics field has witnessed an interesting new trend. Several companies started the production of service robots whose aim is to cooperate with humans. The robots developed so far are either rather expensive or unsuitable for manipulation tasks. This article presents the result of a project which wishes to demonstrate the feasibility of an affordable humanoid robot. R1 is able to navigate, and interact with the environment (grasping and carrying objects, operating switches, opening doors etc). The robot is also equipped with a speaker, microphones and it mounts a display in the head to support interaction using natural channels like speech or (simulated) eye movements. The final cost of the robot is expected to range around that of a family car, possibly, when produced in large quantities, even significantly lower. This goal was tackled along three synergistic directions: use of polymeric materials, light-weight design and implementation of novel actuation solutions. These lines, as well as the robot with its main features, are described hereafter.