We consider dynamic stair climbing with the HRP-4 humanoid robot as part of an Airbus manufacturing use-case demonstrator. We share experimental knowledge gathered so as to achieve this task, which HRP-4 had never been challenged to before. In particular, we extend walking stabilization based on linear inverted pendulum tracking [1] by quadratic programming-based wrench distribution and a whole-body admittance controller that applies both end-effector and CoM strategies. While existing stabilizers tend to use either one or the other, our experience suggests that the combination of these two approaches improves tracking performance. We demonstrate this solution in an on-site experiment where HRP4 climbs an industrial staircase with 18.5 cm high steps, and release our walking controller as open source software.

Model based open-loop and closed-loop control systems make use of the system's inertial parameters. Unfortunately, not all of these values can be determined analytically nor can they be obtained from simple measurements. Established experiments for inertial parameters estimation have been applied to serial and parallel rigid-link manipulators, yet in very few cases to cable-driven parallel robots. Due to their kinematic properties and their unique setup, cable robots are more sensitive to incorrect estimates of the inertial parameters making it important to obtain such quantities through experiments. In this work, we assess the topic of inertial parameter identification of a parallel flexible-link manipulator exemplified by the suspended cable-driven parallel robot CoGIRo. Identification equations are derived from Newton-Euler equations of motion of an arbitrary point fixed to a rigid-body. Laboratory experiments for identification of the inertial parameters are then introduced and results are presented. Within the limitations of the sensors and data acquisition methods, reasonable results have been obtained, thereby validating the procedure for suspended cable-driven parallel robots.

This paper presents a predictive force control approach to compensate for the physiological motion induced by both respiratory and heart beating motions during cardiac surgery. It focuses on the design and implementation of the control algorithm in the context of robotized minimally invasive surgery. The controller is based on a linear predictive control loop using the force information applied on the heart by the instrument. Experimental evaluation highlights the performance of the algorithm for compensating 3D physiological motion.