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The control of a robot in its task space is a standard approach nowadays. If the system is kinematically redundant with respect to this goal, one can even execute additional subtasks simultaneously. By utilizing null space projections, for example, the whole stack of tasks can be implemented within a strict task hierarchy following the order of priority. One of the most common methods to track multiple task-space trajectories at the same time is to feedback-linearize the system and dynamically decouple all involved subtasks, which finally yields the exponential stability of the desired equilibrium. In this article, we provide a hierarchical multi-objective controller for trajectory tracking that ensures both asymptotic stability of the equilibrium and a desired contact impedance at the same time. In contrast to the state of the art in prioritized multi-objective control, feedback of the external forces can be avoided and the natural inertia of the robot is preserved. The controller is evaluated in simulations and on a standard lightweight robot with torque interface. The approach is predestined for precise trajectory tracking where dedicated and robust physical-interaction compliance is crucial at the same time.

In this paper, a new hybrid kinematic/dynamic control scheme for humanoid robots is proposed. Its basic idea lies in the tracking of several values in both operational and joint spaces. These values include (i) the relative pose of the robot’s feet, (ii) the position of the center of mass, (iii) the body’s orientation, and (iv) the admissible range of variation of the joints. A zero-moment point (ZMP)-based dynamic feedback is included in the proposed scheme to improve the stability of dynamic motions. The proposed stabilizer is based on a spherical projection of a nonlinear PID regulation control law. Through the proposed study, it is shown that these objectives allow us to produce smooth dynamically stable whole-body motions. The effectiveness and robustness of the proposed control scheme are demonstrated through four real-time experimental scenarios conducted on the HOAP-3 humanoid robot.

Providing the operator with a good view of the remote site is of paramount importance in aerial telemanipulation. In light of that, this paper proposes the application of a hierarchical control framework in order to tackle the problem of adjusting the field of view of an on-board camera as a secondary task. The proposed approach ensures that the flying base, and consequently the camera, can be steered in order to provide a distant operator with a desired field of view without disturbing the end-effector pose. The approach is focused on aerial manipulators with torque-controlled arms, like the DLR Suspended Aerial Manipulator (SAM), while allowing the base to be directly torque-controlled or, alternatively, through an inner-loop velocity controller. Quantitative, qualitative, and real-scenario experimental validation is carried out using the SAM and confirms the need for such an approach and its efficacy in achieving decoupled field-of-view control.

This paper presents a novel omnidirectional walking pattern generator for bipedal locomotion combining two structurally different approaches based on the virtual constraints and the preview control theories to generate a flexible gait that can be modified on-line. The proposed strategy synchronizes the displacement of the robot along the two planes of walking: the zero moment point based preview control is responsible for the lateral component of the gait, while the sagittal motion is generated by a more dynamical approach based on virtual constraints. The resulting algorithm is characterized by a low computational complexity and high flexibility, requisite for a successful deployment to humanoid robots operating in real world scenarios. This solution is motivated by observations in biomechanics showing how during a nominal gait the dynamic motion of the human walk is mainly generated along the sagittal plane. We describe the implementation of the algorithm and we detail the strategy chosen to enable omnidirectionality and on-line gait tuning. Finally, we validate our strategy through simulation experiments using the COMAN + platform, an adult size humanoid robot developed at Istituto Italiano di Tecnologia. Finally, the hybrid walking pattern generator is implemented on real hardware, demonstrating promising results: the WPG trajectories results in open-loop stable walking in the absence of external disturbances.

This paper introduces a passivity-based control framework for multi-task time-delayed bilateral teleoperation and shared control of kinematically-redundant robots. The proposed method can be seen as extension of state-of-the art hierarchical whole-body control as it allows for some of the tasks to be commanded by a remotely-located human operator through a haptic device while the others are autonomously performed. The operator is able to switch among tasks at any time without compromising the stability of the system. To enforce the passivity of the communication channel as well as to dissipate the energy generated by the null-space projectors used to enforce the hierarchy among the tasks, the Time-Domain Passivity Approach (TDPA) is applied. The efficacy of the approach is demonstrated through its application to the DLR Suspended Aerial Manipulator (SAM) in a real telemanipulation scenario with variable time delay, jitter, and package loss.

This paper presents practical enhancements of the operational space formulation (OSF) to exploit inequality constraints for whole-body control of a high degree of freedom robot with a floating base and multiple contacts, such as humanoids. A task-oriented optimisation method is developed to obtain a feasible torque resolution solely for task variables based on the OSF, which effectively reduces the number of optimisation variables. Interestingly, the proposed scheme amends assigned tasks on demand of satisfying inequality conditions, while dynamic consistency among contact-constrained tasks is preserved. In addition, we propose an efficient algorithm structure ameliorating real-time control capability which has been a major hurdle to transplant optimisation methods into the OSF-based whole-body control framework. Control performance, the feasibility of the optimised solution, and the computation time of the proposed control framework are verified through realistic dynamic simulations of a humanoid. We also clarify the pros and cons of the proposed method compared with existing optimisation-based ones, which may offer an insight for practical control engineers to select whole-body controllers necessitated from the desired application.

Balancing and push-recovery are essential capabilities enabling humanoid robots to solve complex locomotion tasks. In this context, classical control systems tend to be based on simplified physical models and hard-coded strategies. Although successful in specific scenarios, this approach requires demanding tuning of parameters and switching logic between specifically-designed controllers for handling more general perturbations. We apply model-free Deep Reinforcement Learning for training a general and robust humanoid push-recovery policy in a simulation environment. Our method targets high-dimensional whole-body humanoid control and is validated on the iCub humanoid. Reward components incorporating expert knowledge on humanoid control enable fast learning of several robust behaviors by the same policy, spanning the entire body. We validate our method with extensive quantitative analyses in simulation, including out-of-sample tasks which demonstrate policy robustness and generalization, both key requirements towards real-world robot deployment., Co-first authors: Diego Ferigo and Raffaello Camoriano; 8 pages

International audience; In the context of legged robotics, many criteria based on the control of the Center of Mass (CoM) have been developed to ensure stable and safe robot locomotion. Defining a whole-body framework with the control of the CoM requires a planning strategy, often based on a specific type of gait and reliable state-estimation. In a whole-body control approach, if the CoM task is not specified, the consequent redundancy can still be resolved by specifying a postural task that sets references for all the joints. Therefore, the postural task can be exploited to keep a well behaved, stable kinematic configuration. In this work, we propose a generic locomotion framework which is able to generate different kind of gaits, ranging from very dynamic gaits such as the trot, to more static gaits like the crawl, without the need to plan the CoM trajectory. Consequently, the whole-body controller becomes planner-free and it does not require the estimation of the floating base state, which is often prone to drift. The framework is composed of a priority-based whole-body controller that works in synergy with a walking pattern generator. We show the effectiveness of the framework by presenting simulations on different types of simulated terrains, including rough terrain, using different quadruped platforms.

Attaching a robotic manipulator to a flying base allows for significant improvements in the reachability and versatility of manipulation tasks. In order to explore such systems while taking advantage of human capabilities in terms of perception and cognition, bilateral teleoperation arises as a reasonable solution. However, since most telemanipulation tasks require visual feedback in addition to the haptic one, real-time (task-dependent) positioning of a video camera, which is usually attached to the flying base, becomes an additional objective to be fulfilled. Since the flying base is part of the kinematic structure of the robot, if proper care is not taken, moving the video camera could undesirably disturb the end-effector motion. For that reason, the necessity of controlling the base position in the null space of the manipulation task arises. In order to provide the operator with meaningful information about the limits of the allowed motions in the null space, this paper presents a novel haptic concept called Null-Space Wall. In addition, a framework to allow stable bilateral teleoperation of both tasks is presented. Numerical simulation data confirm that the proposed framework is able to keep the system passive while allowing the operator to perform time-delayed telemanipulation and command the base to a task-dependent optimal pose., Comment: to be published in 2020 IEEE International Conference on Robotics and Automation (ICRA)

Light-weight robotic manipulators in combination with power wheelchairs can help to restore the mobility of people with disabilities. While such systems are available on the market, they typically are limited to fully manual control modes. In research, shared control methods are employed, to increase the usability of these systems. Here, we present an additional extension, by introducing a whole-body control concept to the assistive robotic system EDAN. Combined with shared control, the whole-body controller allows the realization of complex tasks which necessitate the coordination of arm and platform, while ensuring compliant behavior resulting from the impedance control law. The implemented approach is analyzed and validated in an exemplary task of opening a door, passing through it and closing it afterwards. While this task would exceed the reachability of the arm in a classical approach, the combination of whole-body control with a shared control scheme allows for quick and efficient execution.

This work presents an extension of balance control for torque-controlled humanoid robots. Within a non-strict task hierarchy, the controller allows the robot to use the feet end-effectors to balance, while the remaining hand end-effectors can be used to perform Dual-Arm manipulation. The controller generates a passive and compliance behaviour to regulate the location of the centre of mass (CoM), the orientation of the hip and the poses of each end-effector assigned to the task of interaction (in this case bi-manipulation). Then, an appropriate wrench (force and torque) is applied to each of the end-effectors employed for the task to achieve this purpose. Now, in this new controller, the essential requirement focuses on the fact that the desired wrench in the CoM is computed through the sum of the balancing and bi-manipulation wrenches. The bi-manipulation wrenches are obtained through a new dynamic model that allows executing tasks of approaching the grip and manipulation of large objects compliantly. On the other hand, the feedback controller has been maintained but in combination with a bi-manipulation-oriented feedforward control to improve the performance in the object trajectory tracking. This controller is tested in different experiments with the robot TORO. This project has received funding from the European Research Council (ERC) (grant agreement No. 819358), from the HUMASOFT (DPI2016- 75330-P) and RoboCity2030-DIH-CM (S2018/NMT-4331).

International audience; Research into legged robotics is primarily motivated by the prospects of building machines that are able to navigate in challenging and complex environments that are predominantly non-flat. In this context, control of contact forces is fundamental to ensure stable contacts and equilibrium of the robot. In this paper we propose a planning/control framework for quasi-static walking of quadrupedal robots, implemented for a demanding application in which regulation of ground reaction forces is crucial. Experimental results demonstrate that our 75-kg quadruped robot is able to walk inside two high-slope (50 degrees) V-shaped walls; an achievement that to the authors' best knowledge has never been presented before. The robot distributes its weight among the stance legs so as to optimize user-defined criteria. We compute joint torques that result in no foot slippage, fulfillment of the unilateral constraints of the contact forces and minimization of the actuators effort. The presented study is an experimental validation of the effectiveness and robustness of QP-based force distributions methods for quasi-static locomotion on challenging terrain.

The task of robotic mobile manipulation poses several scientific challenges that need to be addressed to execute complex manipulation tasks in unstructured environments, in which collaboration with humans might be required. Therefore, we present ALMA, a motion planning and control framework for a torque-controlled quadrupedal robot equipped with a six degrees of freedom robotic arm capable of performing dynamic locomotion while executing manipulation tasks. The online motion planning framework, together with a whole-body controller based on a hierarchical optimization algorithm, enables the system to walk, trot and pace while executing operational space end-effector control, reactive human-robot collaboration and torso posture optimization to increase the arm’s workspace. The torque control of the whole system enables the implementation of compliant behavior, allowing a user to safely interact with the robot. We verify our framework on the real robot by performing tasks such as opening a door and carrying a payload together with a human., 2019 International Conference on Robotics and Automation (ICRA), ISBN:978-1-5386-6027-0, ISBN:978-1-5386-6026-3, ISBN:978-1-5386-8176-3

We present experimental results using a passive whole-body control approach for quadruped robots that achieves dynamic locomotion while compliantly balancing the robot's trunk. We formulate the motion tracking as a Quadratic Program (QP) that takes into account the full robot rigid body dynamics, the actuation limits, the joint limits and the contact interaction. We analyze the controller's robustness against inaccurate friction coefficient estimates and unstable footholds, as well as its capability to redistribute the load as a consequence of enforcing actuation limits. Additionally, we present practical implementation details gained from the experience with the real platform. Extensive experimental trials on the 90 kg Hydraulically actuated Quadruped (HyQ) robot validate the capabilities of this controller under various terrain conditions and gaits. The proposed approach is superior for accurate execution of highly dynamic motions with respect to the current state of the art., 9 pages, 9 figures

Autonomous behaviors in humanoid robots are generally implemented by considering the robot as two separate parts, using the lower body for locomotion and balancing, and the upper body for manipulation actions. This paper provides a unified framework for autonomous grasping with bipedal robots using a compliant whole-body controller. The grasping action is based on parametric grasp planning for unknown objects using shape primitives, which allows a generation of multiple grasp poses on the object. A reach ability analysis is used to select the final grasp, and also for triggering a base repositioning behavior that locates the robot on a better position for grasping the desired object more confidently, considering all grasps and the uncertainty in reaching the desired position. The whole-body controller accounts for perturbations at any level and ensures a successful execution of the intended task. The approach is implemented in the humanoid robot TORO, and different experiments demonstrate its robustness and flexibility.

Planning whole-body motions while taking into account the terrain conditions is a challenging problem for legged robots since the terrain model might produce many local minima. Our coupled planning method uses stochastic and derivatives-free search to plan both foothold locations and horizontal motions due to the local minima produced by the terrain model. It jointly optimizes body motion, step duration and foothold selection, and it models the terrain as a cost-map. Due to the novel attitude planning method, the horizontal motion plans can be applied to various terrain conditions. The attitude planner ensures the robot stability by imposing limits to the angular acceleration. Our whole-body controller tracks compliantly trunk motions while avoiding slippage, as well as kinematic and torque limits. Despite the use of a simplified model, which is restricted to flat terrain, our approach shows remarkable capability to deal with a wide range of non-coplanar terrains. The results are validated by experimental trials and comparative evaluations in a series of terrains of progressively increasing complexity., Comment: 15 pages, pre-print, journal

Whole-body controllers have become a well adopted control paradigm for humanoid robots, uniting communities working on control systems and sensor-based techniques. These communities have focused on developing simple design tools and adopting common math representations. As such, we are now closer than ever towards sharing common techniques that work across platforms, and making the process of designing controllers as easy as possible, with the hope of one day to completely automate the controller design process for all types of humanoid robots. At the same time, current whole-body controllers suffer from important limitations that prevent them from achieving highly agile dynamic behaviors, operating with high mechanical and electronic efficiency, be safe at high speeds in terms of human-robot interactions, and be easy to deploy with minimal designer tuning. This Chapter aims to explore various key issues in whole-body control of humanoid robots, including: i) providing an in-depth overview of state-of-the-art techniques, ii) providing a list of references to open source software implementations, iii) discussing performance differences between the various types of architectures, iv) analyzing limitations and future research directions for optimal performance.

Compliant manipulation is one of the grand challenges for autonomous robots. Many household chores in human environments, such as cleaning the floor or wiping windows, rely on this principle. At the same time these tasks often require whole-body motions to cover a larger workspace. The performance of the actual task itself is thereby dependent on a large number of parameters that have to be taken into account. To tackle this issue we propose to utilize low-level compliant whole-body control strategies parameterized by high-level hybrid reasoning mechanisms. We categorize compliant wiping actions in order to determine relevant control parameters. According to these parameters we set up process models for each identified wiping action and implement generalized control strategies based on human task knowledge. We evaluate our approach experimentally on three whole-body manipulation tasks, namely scrubbing a mug with a sponge, skimming a window with a window wiper and bi-manually collecting the shards of a broken mug with a broom.

Humanoid service robots in domestic environments have to interact with humans and their surroundings in a safe and reliable way. One way to manage that is to equip the robotic systems with force-torque sensors to realize a physically compliant whole-body behavior via impedance control. To provide mobility, such robots often have wheeled platforms. The main advantage is that no balancing effort has to be made compared to legged humanoids. However, the nonholonomy of most wheeled systems prohibits the direct implementation of impedance control due to kinematic rolling constraints that must be taken into account in modeling and control. In this paper we design a whole-body impedance controller for such a robot, which employs an admittance interface to the kinematically controlled mobile platform. The upper body impedance control law, the platform admittance interface, and the compensation of dynamic couplings between both subsystems yield a passive closed loop. The convergence of the state to an invariant set is shown. To prove asymptotic stability in the case of redundancy, priority-based approaches can be employed. In principle, the presented approach is the extension of the well-known and established impedance controller to mobile robots. Experimental validations are performed on the humanoid robot Rollin' Justin. The method is suitable for compliant manipulation tasks with low-dimensional planning in the task space.

International audience; Although in recent years there have been quite a few studies aimed at the navigation of robots in cluttered environments, few of these have addressed the problem of robots navigating while moving a large or heavy objects. This is especially useful when transporting loads with variable weights and shapes without having to change the robot hardware. Inspired by the wide use of makeshift carts by humans, we tackle, in this work, the problem of a humanoid robot navigating in a cluttered environment while displacing a heavy load that lies on a cart-like object. We present a complete navigation scheme, from the incremental construction of a map of the environment and the computation of collision-free trajectories to the control to execute these trajectories. Our contributions are as follows: (1) a whole-body control scheme that makes the humanoid use its hands and arms to control the motions of the cart-load system (e.g. tight turns) (2) a sensorless approach to automatically select the appropriate primitive set according to the load weight (3) a motion planning algorithm to find an obstacle-free trajectory using the appropriate primitive set and the constructed map of the environment as input (4) an efficient filtering technique to remove the cart from the field of view of the robot while improving the general performances of the SLAM algorithms and (5) a continuous and consistent odometry data formed by fusing the visual and the robot odometry information. We present experiments conducted on a real Nao robot, equipped with an RGB-D sensor mounted on its head, pushing a cart with different loads. Our experiments show that the payload can be significantly increased without changing the robot’s main hardware, and therefore enacting the capacity of humanoid robots in real-life situations.

In this work we present a novel method to address the balancing problem for torque controlled legged robots through post-optimization of contact forces. The main concept consists in treating a legged robot as a fully actuated fixed-base system in order to compute the desired joint torques according to a fixed-based torque controller. The under-actuated component of the obtained torques is then mapped into contact forces through an optimal distribution problem. Besides extending previous work to the floating-base case, the proposed method has the notable advantage of avoiding the specification of a desired momentum of rotation, in addition to a reduced number of decision variables compared to full-inverse dynamics methods. The effectiveness of our approach has been validated in simulation using two different humanoid platforms: the CEN-TAURO and the COMAN+ robots, both recently developed at Istituto Italiano di Tecnologia (IIT). Preliminary experimental results on COMAN+ are also presented.

International audience; Although in recent years there have been quite a few studies aimed at the navigation of robots in cluttered environments, few of these have addressed the problem of robots navigating while moving a large or heavy object. Such a functionality is especially useful when transporting objects of different shapes and weights without having to modify the robot hardware. In this work, we tackle the problem of making two humanoid robots navigate in a cluttered environment while transporting a very large object that simply could not be moved by a single robot. We present a complete navigation scheme, from the incremental construction of a map of the environment and the computation of collision-free trajec-tories to the design of the control to execute those trajectories. We present experiments made on real Nao robots, equipped with RGB-D sensors mounted on their heads, moving an object around obstacles. Our experiments show that a significantly large object can be transported without modifying the robot main hardware, and therefore that our scheme enhances the humanoid robots capacities in real-life situations. Our contributions are: (1) a low-dimension multi-robot motion planning algorithm that finds an obstacle-free trajectory, by using the constructed map of the environment as an input, (2) a framework that produces continuous and consistent odometry data, by fusing the visual and the robot odometry information, (3) a synchronization system that Two Nao robots holding an object together. We propose a motion planning strategy for the Nao-object-Nao system to navigate in a cluttered environment and a synchronization approach that allows the motions done by the two Naos without compromising the whole system stability because of the robots swinging movement. uses the projection of the robots based on their hands positions coupled with the visual feedback error computed from a frontal camera, (4) an efficient real-time whole-body control scheme that controls the motions of the closed-loop robot-object-robot system.

International audience; Robots acting in human environments usually need to perform multiple motion and force tasks while respecting a set of constraints. When a physical contact with the environment is established, the newly activated force task or contact constraint may interfere with other tasks. The objective of this paper is to provide a control framework that can achieve real-time control of humanoid robots performing both strict and non strict prioritized motion and force tasks. It is a torque-based quasi-static control framework, which handles a dynamically changing task hierarchy with simultaneous priority transitions as well as activation or deactivation of tasks. A quadratic programming problem is solved to maintain desired task hierarchies, subject to constraints. A generalized projector is used to quantitatively regulate how much a task can influence or be influenced by other tasks through the modulation of a priority matrix. By the smooth variations of the priority matrix, sudden hierarchy rearrangements can be avoided to reduce the risk of instability. The effectiveness of this approach is demonstrated on both a simulated and a real humanoid robot.

This paper presents an analysis of the relation between effort and gravitational stiffness, two physical measures that depend on the configuration of the robot. It is shown that whenever the gravitational stiffness is maximized, the effort is indirectly minimized. A minimum effort attractor that controls the gravitational stiffness is presented. This attractor together with an attractor to zero joint momentum guarantee balance maintenance. The novel implementation of the attractor-based Whole-body Motion Control (WBMC) System is experimentally tested: first with simple models, and finally with the full-body humanoid robot COMAN in a physical simulation.

International audience; This paper details the implementation of state-of-the-art whole-body control algorithms on the humanoid robot iCub. We regulate the forces between the robot and its surrounding environment to stabilize a desired posture. We assume that the forces and torques are exerted on rigid contacts. The validity of this assumption is guaranteed by constraining the contact forces and torques, e.g., the contact forces must belong to the associated friction cones. The implementation of this control strategy requires the estimation of both joint torques and external forces acting on the robot. We then detail algorithms to obtain these estimates when using a robot with an iCub-like sensor set, i.e., distributed six-axis force-torque sensors and whole-body tactile sensors. A general theory for identifying the robot inertial parameters is also presented. From an actuation standpoint, we show how to implement a joint-torque control in the case of DC brushless motors. In addition, the coupling mechanism of the iCub torso is investigated. The soundness of the entire control architecture is validated in a real scenario involving the robot iCub balancing and making contact with both arms.

International audience; Whole-body controllers provide the tools to execute multiple simultaneous tasks on humanoid robots, but given the robot's internal and external constraints, interferences are often generated which impede task completion. Priorities can be assigned to each task to manage these interferences, unfortunately, this is often done at the detriment of one or more tasks. In this paper we present a novel framework for defining and optimizing multiple tasks in order to resolve potential interferences prior to task execution and remove the need for prioritization. Our framework parameterizes tasks with Dynamical Movement Primitives, simulates and evaluates their execution, and optimizes their parameters based on a general compatibility principle, which is independent of the robot's topology, tasks or environment. Two test cases on a simulation of a humanoid robot are used to demonstrate the successful optimization of initially interfering tasks using this framework.

Research in whole-body control (WBC) aims to contribute to provide robots with those capabilities that are necessary to move and perform in real world scenarios. Until recent years, limitations on hardware relegated WBC to almost purely theoretical research. Recently, a growing number of experimental platforms have become available (in particular, torque-controlled humanoids). This new opportunity has triggered the deployment on real robots of the theoretical outcomes of research in the field. This is backed up by a number of new research projects and initiatives addressing issues in this domain, including the Darpa robotic challenge (DRC). The goal of this special issue is to provide a clear representation of what is the state-of-the-art in WBC, and to help identifying what steps still need to be taken to have humanoid robots moving out of research laboratories to real world applications.

Whole-body control (WBC) systems represent a wide range of complex movement skills in the form of low-dimensional task descriptors which are projected on to the robot’s actuator space. Using these methods allow to exploit the full capabilities of the entire body of redundant, floating-base robots in compliant multi-contact interaction with the environment, to execute any single task and simultaneous multiple tasks. This paper presents an attractor-based whole-body motion control (WBMC) system, developed for torque-control of floating-base robots. The attractors are defined as atomic control modules that work in parallel to, and independently from the other attractors, generating joint torques that aim to modify the state of the robot so that the error in a target condition is minimized. Balance of the robot is guaranteed by the simultaneous activation of an attractor to the minimum effort configuration, and of an attractor to a zero joint momentum. A novel formulation of the minimum effort is proposed based on the assumption that whenever the gravitational stiffness is maximized, the effort is consequently minimized. The effectiveness of the WBMC was experimentally demonstrated with the COMAN humanoid robot in a physical simulation, in scenarios where multiple conflicting tasks had to be accomplished simultaneously.