The task of performing locomotion and manipulation simultaneously poses several scientific challenges, such as how to deal with the coupling effects between them and how to cope with unknown disturbances introduced by manipulation. This paper presents an inverse dynamics-based whole-body controller for a torque-controlled quadrupedal manipulator capable of performing locomotion while executing manipulation tasks. Unlike existing methods that deal with locomotion and manipulation separately, the proposed controller can handle them uniformly, which can take into account the coupling effects between the base, limbs and manipulated object. The controller tracks the desired task–space motion references based on a hierarchical optimization algorithm, given a set of hierarchies that define strict priorities and the importance of weighting each task within a hierarchy. The simulation results show the robot is able to follow multiple task–space motion reference trajectories with reasonable deviation, which proved the effectiveness of the proposed controller.

In this work, we present a highly functional teleoperation system, that integrates a full-body inertia-based motion capture suit and three intuitive teleoperation strategies with a Whole-Body Control (WBC) framework, for quadrupedal legged manipulators. This enables the realisation of commands from the teleoperator that would otherwise not be possible, as the framework is able to utilise DoF redundancy to meet several objectives simultaneously, such as locking the gripper frame in position while the trunk completes a task. This is achieved through the WBC framework featuring a defined optimisation problem that solves a range of Cartesian and joint space tasks, while subject to a set of constraints (e.g. halt constraints). These tasks and constraints are highly modular and can be configured dynamically, allowing the teleoperator to switch between teleoperation strategies seamlessly. The overall system has been tested and validated through a physics-based simulation and a hardware test, demonstrating all functionality of the system, which in turn has been used to evaluate its effectiveness.

This article presents a standardized human-robot teleoperation interface (HRTI) evaluation scheme for mobile manipulators. Teleoperation remains the predominant control type for mobile manipulators in open environments, particularly for quadruped manipulators. However, mobile manipulators, especially quadruped manipulators, are relatively novel systems to be implemented in the industry compared to traditional machinery. Consequently, no standardized interface evaluation method has been established for them. The proposed scheme is the first of its kind in evaluating mobile manipulator teleoperation. It comprises a set of robot motion tests, objective measures, subjective measures, and a prediction model to provide a comprehensive evaluation. The motion tests encompass locomotion, manipulation, and a combined test. The duration for each trial is collected as the response variable in the objective measure. Statistical tools, including mean value, standard deviation, and T-test, are utilized to cross-compare between different predictor variables. Based on an extended Fitts' law, the prediction model employs the time and mission difficulty index to forecast system performance in future missions. The subjective measures utilize the NASA-task load index and the system usability scale to assess workload and usability. Finally, the proposed scheme is implemented on a real-world quadruped manipulator with two widely-used HRTIs, the gamepad and the wearable motion capture system.

This paper presents a motion-capture based control framework for the purpose of effectively teleoperating two legged manipulators without significant delays caused by the switching of controllers. The control framework generates high-level trajectories in 6 degrees-of-freedom and uses finger gesture detection to act as triggers in selecting which robot to control as well as toggling various aspects of control such as yaw rotation of the quadruped platform. The functionality and ease of use of the control framework is demonstrated through a real life experiment where the operator controls two quadrupedal manipulator robots to open a spray can. The experiment was successfully accomplished by the proposed teleoperation framework.

This paper presents a comparison study of three control design approaches for humanoid balancing based on the Center of Mass (CoM) stabilization and body posture adjustment. The comparison was carried out under controlled circumstances allowing other researchers to replicate and compare our results with their own. The feedback control from state space design is based on simple models and provides sufficient robustness to control complex and high Degrees of Freedom (DoFs) systems, such as humanoids. The implemented strategies allow compliant behavior of the robot in reaction to impulsive or periodical disturbances, resulting in a smooth and human-like response while considering constraints. In this respect, we implemented two balancing strategies to compensate for the CoM deviation. The first one uses the robot’s capture point as a stability principle and the second one uses the Force/Torque sensors at the ankles to define a CoM reference that stabilizes the robot. In addition, was implemented a third strategy based on upper body orientation to absorb external disturbances and counterbalance them. Even though the balancing strategies are implemented independently, they can be merged to further increase balancing performance. The proposed strategies were previously applied on different humanoid bipedal platforms, however, their performance could not be properly benchmarked before. With this concern, this paper focuses on benchmarking in controlled scenarios to help the community in comparing different balance techniques. The key performance indicators (KPIs) used in our comparison are the CoM deviation, the settling time, the maximum measured orientation, passive gait measure, measured ankles torques, and reconstructed Center of Pressure (CoP). The benchmarking experiments were carried out in simulations and using the facility at Istituto Italiano di Tecnologia on the REEM-C humanoid robot provided by PAL robotics inside the EU H2020 project EUROBENCH framework.