Quadratic Programs (QPs) are widely used in the control of walking robots, especially in Model Predictive Control (MPC) and Whole-Body Control (WBC). In both cases, the controller design requires the formulation of a QP and the selection of a suitable QP solver, both requiring considerable time and expertise. While computational performance benchmarks exist for QP solvers, studies comparing optimal combinations of computational hardware (HW), QP formulation, and solver performance are lacking. In this work, we compare dense and sparse QP formulations, and multiple solving methods on different HW architectures, focusing on their computational efficiency in dynamic walking of four-legged robots using MPC. We introduce the Solve Frequency per Watt (SFPW) as a performance measure to enable a cross-hardware comparison of the efficiency of QP solvers. We also benchmark different QP solvers for WBC that we use for trajectory stabilization in quadrupedal walking. As a result, this paper recommends a starting point for practitioners on the selection of QP formulations and solvers for different HW architectures in walking robots and indicates which problems should be devoted the greater technical effort.

In robotics, structural design and behavior optimization have long been considered separate processes, resulting in the development of systems with limited capabilities. Recently, co-design methods have gained popularity, where bi-level formulations are used to simultaneously optimize the robot design and behavior for specific tasks. However, most implementations assume a serial or tree-type model of the robot, overlooking the fact that many robot platforms incorporate parallel mechanisms. In this paper, we present a first co-design formulation that explicitly incorporates parallel coupling constraints into the dynamic model of the robot. In this framework, an outer optimization loop focuses on the design parameters, in our case the transmission ratios of a parallel belt-driven manipulator, which map the desired torques from the joint space to the actuation space. An inner loop performs trajectory optimization in the actuation space, thus exploiting the entire dynamic range of the manipulator. We compare the proposed method with a conventional co-design approach based on a simplified tree-type model. By taking advantage of the actuation space representation, our approach leads to a significant increase in dynamic payload capacity compared to the conventional co-design implementation.

Mobile robots are nowadays frequently used for interaction tasks in the real world, e.g. for opening doors or for pick-and-place tasks. When used in real-world environments, adapting the robot controllers to uncertain contact dynamics is a significant challenge. Adaptive Model Predictive Control (AMPC) is an approach for controlling robot motions while adapting to uncertain or changing dynamics. However, most of the existing AMPC approaches used in mobile manipulation require either expert tuning or extensive training, making it very difficult to introduce novel or diverse tasks. In addition, the adjustment of several, independent environment parameters is usually not considered in the AMPC formulation. In this work, we introduce a hierarchical approach that uses Gaussian Mixture Models (GMMs) and Gaussian Mixture Regression (GMR) to predict the dynamic model parameters of MPC based on proprioceptive measurements and perform tasks with multiple unknown environmental parameters. The approach is evaluated in simulation and in real experiments on a mobile manipulator and compared to several baseline methods. It is shown that it outperforms standard MPC and an existing AMPC approach on several tasks such as carrying, pushing, and door opening.

It is well established that a stiff structure along with an optimal mass distribution are key features to perform dynamic movements, and parallel designs provide these characteristics to a robot. This work presents the new upper-body design of the humanoid robot RH5 named RH5 Manus with series-parallel hybrid design. The new design choices allow us to perform dynamic motions including tasks that involve a payload of 4 kg in each hand and fast boxing motions. The parallel kinematics combined with an overall serial chain of the robot provides us with high force production along with a larger range of motion and low peripheral inertia. The robot is equipped with backdrivable actuators with current sensing, force-torque sensors, stereo camera, laser scanners, high-resolution encoders etc that provide interaction with operators and environment. We generate several diverse dynamic motions using trajectory optimization, and successfully execute them on the robot with accurate trajectory and velocity tracking, while respecting joint rotation, velocity, and torque limits.

Parallel mechanisms are becoming increasingly popular as subsystems in various robots due to their superior stiffness, payload-to-weight ratio, and dynamic properties. The serial connection of parallel subsystems leads to series-parallel hybrid robots, which are more difficult to model and control than serial or tree-type systems. At the same time, Whole-Body Control (WBC) has become the method of choice in the control of robots with redundant degrees of freedom, e.g., legged robots. However, most state-of-the-art WBC frameworks can only deal with serial or tree-type robot topologies. In this paper, we describe a computationally efficient framework for Whole-Body Control of series-parallel hybrid robots subjected to a large number of holonomic constraints. In contrast to existing WBC frameworks, our approach describes the optimization problem in the actuation space of a series-parallel robot, which provides better exploitation of the feasible workspace, higher accuracy, and more transparent behavior near singularities. We evaluate the proposed framework on two different humanoids with series-parallel architecture and compare its performance to a WBC approach for tree-type robots.

This video paper describes the practical outcome of the first milestone of a project aiming at setting up a so-called Hybrid Team that can accomplish a wide variety of different tasks. In general, the aim is to realize and examine the collaboration of augmented humans with autonomous robots, virtual characters and SoftBots (purely software based agents) working together in a Hybrid Team to accomplish common tasks. The accompanying video shows a customized packaging scenario and can be downloaded from http://hysociatea.dfki.de/?p=441.

This paper presents the design of the robot AILA, a mobile dual-arm robot system developed as a research platform for investigating aspects of the currently booming multidisciplinary area of mobile manipulation. The robot integrates and allows in a single platform to perform research in most of the areas involved in autonomous robotics: navigation, mobile and dual-arm manipulation planning, active compliance and force control strategies, object recognition, scene representation, and semantic perception. AILA has 32 degrees of freedom, including 7-DOF arms, 4-DOF torso, 2-DOF head, and a mobile base equipped with six wheels, each of them with two degrees of freedom. The primary design goal was to achieve a lightweight arm construction with a payload-to-weight ratio greater than one. Besides, an adjustable body should sustain the dual-arm system providing an extended workspace. In addition, mobility is provided by means of a wheel-based mobile base. As a result, AILA's arms can lift 8kg and weigh 5.5kg, thus achieving a payload-to-weight ratio of 1.45. The paper will provide an overview of the design, especially in the mechatronics area, as well as of its realization, the sensors incorporated in the system, and its control software.

In this paper a vision system for robust feature extraction and 3D reconstruction of boundary segmented objects is presented. The goal of the system is reliable perception of a professional life environment in a scenario of the rehabilitation robot FRIEND. Reconstructed scenes are used to plan object manipulation with a 7-DoF manipulator arm. The robustness of boundary feature extraction is achieved by the means of including feedback control at image segmentation level. The objective of feedback is to adjust the segmentation parameters in order to cope with scene uncertainties, such as variable illumination conditions. Robustly extracted 2D object features are provided as input to the 3D object reconstruction module of the FRIEND vision system. The performance of the proposed approach is evaluated through experiments in the Library scenario of the robotic system FRIEND.