This paper presents the first successful experiment implementing whole-body model predictive control with state feedback on a torque-control humanoid robot. We demonstrate that our control scheme is able to do whole-body target tracking, control the balance in front of strong external perturbations and avoid collision with an external object. The key elements for this success are threefold. First, optimal control over a receding horizon is implemented with Crocoddyl, an optimal control library based on differential dynamics programming, providing state-feedback control in less than 10 ms. Second, a warm start strategy based on memory of motion has been implemented to overcome the sensitivity of the optimal control solver to initial conditions. Finally, the optimal trajectories are executed by a low-level torque controller, feedbacking on direct torque measurement at high frequency. This paper provides the details of the method, along with analytical benchmarks with the real humanoid robot Talos.A video of the experiment is available at https://peertube.laas.fr/videos/watch/cbc25927-337c-4635-a1bc-153b9aeb4135

One of the great challenges around the advent of driver assistance systems is to ensure that drivers understand the true capability of technology, such that they can behave accordingly for safe vehicle operation. This understanding can be influenced by a range of factors including vehicle instructions, user interface and warnings, and system control behavior. Validation accounting for these important aspects is therefore central to understanding and comparing safety performance for real world use for overall system design implementations.This paper presents a test methodology specified for implementation on an automotive proving ground facility capturing pre-use information, and driver-vehicle interaction during assisted driving regarding user interface and system control behavior. Data collection was defined around the quantification of driver engagement with the driving task using subjective measures to assess progressive effects of system use and objective metrics considering driver behavior and capability to respond to an emergency scenario.In a pilot assessment, a between-subjects test was conducted using two vehicles with differing assisted driving concepts. A sample of naïve drivers (n=39) was recruited and, following a customer focused description of system functionality, was instructed to drive on a test track in continuous highway driving scenario with longitudinal and lateral driver assistance features active. Subsequently, a critical ‘cut-out’ event was presented requiring a driver response to avoid an in-lane obstacle.Results indicate variability in how drivers interact with the system during ‘normal driving’ with subjective measures demonstrating differences in metrics associated with engagement. Likewise, objective measures for driver reaction to the critical event signify differing levels of driver vigilance associated with perceived functionality of individual systems.Outcomes from this experimental test mark a step in the development of test methods for global assistance system assessment and provide a platform for further progression and refinement of tests. This has implications system design verification with highly replicability whilst accounting for use by representative drivers, alongside possible applications in consumer and regulatory testing with representative drivers.