An Admittance Control Method Based on Parameters Fuzzification for Humanoid Steering Wheel Manipulation

Developing a human bionic manipulator to achieve certain humanoid behavioral skills is a rising problem. Enabling robots to operate the steering wheel to drive the vehicle is a challenging task. To address the problem, this work designs a novel 7-DOF (degree of freedom) humanoid manipulator based on the arm structure of human bionics. The 3-2-2 structural arrangement of the motors and the structural modifications at the wrist allow the manipulator to act more similar to a man. Meanwhile, to manipulate the steering wheel stably and compliantly, an admittance control approach is firstly applied for this case. Considering that the system parameters vary in configuration, we further introduce parameter fuzzification for admittance control. Designed simulations were carried out in Coppeliasim to verify the proposed control approach. As the result shows, the improved method could realize compliant force control under extreme configurations. It demonstrates that the humanoid manipulator can twist the steering wheel stably even in extreme configurations. It is the first exploration to operate a steering wheel similar to a human with a manipulator by using admittance control.

Vision-force-fused curriculum learning for robotic contact-rich assembly tasks

Contact-rich robotic manipulation tasks such as assembly are widely studied due to their close relevance with social and manufacturing industries. Although the task is highly related to vision and force, current methods lack a unified mechanism to effectively fuse the two sensors. We consider coordinating multimodality from perception to control and propose a vision-force curriculum policy learning scheme to effectively fuse the features and generate policy. Experiments in simulations indicate the priorities of our method, which could insert pegs with 0.1 mm clearance. Furthermore, the system is generalizable to various initial configurations and unseen shapes, and it can be robustly transferred from simulation to reality without fine-tuning, showing the effectiveness and generalization of our proposed method. The experiment videos and code will be available at https://sites.google.com/view/vf-assembly.

Teleoperated Surgical Robot with Adaptive Interactive Control Architecture for Tissue Identification

The remote perception of teleoperated surgical robotics has been a critical issue for surgeons in fulfilling their remote manipulation tasks. In this article, an adaptive teleoperation control framework is proposed. It provides a physical human-robot interaction interface to enhance the ability of the operator to intuitively perceive the material properties of remote objects. The recursive least square (RLS) is adopted to estimate the required human hand stiffness that the operator can achieve to compensate for the contact force. Based on the estimated stiffness, a force feedback controller is designed to avoid the induced motion and to convey the haptic information of the slave side. The passivity of the proposed teleoperation system is ensured by the virtual energy tank. A stable contact test validated that the proposed method achieved stable contact between the slave robot and the hard environment while ensuring the transparency of the force feedback. A series of human subject experiments was conducted to empirically verify that the proposed teleoperation framework can provide a more smooth, dexterous, and intuitive user experience with a more accurate perception of the mechanical property of the interacted material on the slave side, compared to the baseline method. After the experiment, the design idea about the force feedback controller of the bilateral teleoperation is discussed.

Collision avoidance analysis of human-robot physical interaction based on null-space impedance control of a dynamic reference arm plane

When the terminal upper limb rehabilitation robot is used for motion-assisted training, collisions between the manipulator links and the human upper limb may occur due to the null-space self-motion of the redundant manipulator. A null-space impedance control method based on a dynamic reference arm plane is proposed to realize collision avoidance during human-robot physical interaction motion for the collision problem between the manipulator links and the human upper limb. Firstly, a dynamic model and a Cartesian impedance controller of the manipulator are established. Then, the null-space impedance controller of the redundant manipulator is established based on the dynamic reference plane, which manages the null-space self-motion of the redundant manipulator to prevent collision between the manipulator links and the human upper limb. Finally, it is experimentally verified that the method proposed in this paper can effectively manage the null-space self-motion of the redundant manipulator, and thus achieve collision avoidance during the human-robot physical interaction motion. This research has significant potential in improving the safety and feasibility of motion-assisted training with rehabilitation robots.

PREDICTOR: A Physical emulatoR enabling safEty anD ergonomICs evaluation and Training of physical human-rObot collaboRation

Safety and ergonomics of Physical Human-Robot Collaboration (PHRC) are crucial to make human-robot collaborative systems trustworthy and make a significant impact in real-world applications. One big obstacle to the development of relevant research is the lack of a general platform for evaluating the safety and ergonomics of proposed PHRC systems. This paper aims to create a Physical emulatoR enabling safEty anD ergonomICs evaluation and Training of physical human-rObot collaboRation (PREDICTOR). PREDICTOR consists of a dual-arm robot system and a VR headset as its hardware and contains physical simulation, haptic rendering and visual rendering modules as its software. The dual-arm robot system is used as an integrated admittance-type haptic device, which senses the force/torque applied by a human operator as an input to drive the simulation of a PHRC system and constrains the handles' motion to match their virtual counterparts in the simulation. The motion of the PHRC system in the simulation is fed back to the operator through the VR headset. PREDICTOR combines haptics and VR to emulate PHRC tasks in a safe environment since the interactive forces are monitored to avoid any risky events. PREDICTOR also brings flexibility as different PHRC tasks can be easily set up by changing the PHRC system model and the robot controller in the simulation. The effectiveness and performance of PREDICTOR were evaluated by experiments.

The motor and the brake of the trailing leg in human walking: transtibial amputation limits ankle-knee torque covariation

Lower-limb amputation limits inherent motor abundance in the locomotor system and impairs walking mechanics. Able-bodied walkers vary ankle torque to adjust step-to-step leg force production as measured by resultant ground reaction forces. Simultaneously, knee torque covaries with ankle torque to act as a brake, resulting in consistent peak leg power output measured by external mechanical power generated on the center of mass. Our objective was to test how leg force control during gait is affected by joint torque variance structure in the amputated limb. Within the framework of the uncontrolled manifold analysis, we measured the Index of Motor Abundance (IMA) to quantify joint torque variance structure of amputated legs and its effect on leg force, where IMA > 0 indicates a stabilizing structure. We further evaluated the extent to which IMA in amputated legs used individual (INV) and coordinated (COV) joint control strategies. Amputated legs produced IMA and INV values similar to intact legs, indicating that torque deviations of the prosthetic ankle can modulate leg force at the end of stance phase. However, we observed much lower COV values in the amputated leg relative to intact legs indicating that biological knee joint torque of the amputated leg does not covary with prosthetic ankle torque. This observation suggests inter-joint coordination during gait is significantly limited as a result of transtibial amputation and may help explain the higher rate of falls and impaired balance recovery in this population, pointing to a greater need to focus on inter-joint coordination within the amputated limb.

Neural-learning-enhanced Cartesian Admittance control of robot with moving RCM constraints

In this manuscript, a scheme for neural-learning-enhanced Cartesian Admittance control is presented for a robotic manipulator to deal with dynamic environments with moving remote center of motion (RCM) constraints. Although some research has been implemented to address fixed constrained motion, the dynamic moving movement constraint is still challenging. Indeed, the moving active RCM constraints generate uncertain disturbance on the robot tool shaft with unknown dynamics. The neural-learning-enhanced decoupled controller with disturbance optimisation is employed and implemented to maintain the performance under the kinematic uncertain and dynamic uncertain generated. In addition, the admittance Cartesian control method is introduced to control the robot, providing compliant behaviour to an external force in its operational space. In this proposed framework, a neural-learning-enhanced disturbance observer is investigated to calculate the external factor operating on the end effector premised on generalised momentum in order to ensure accuracy. Finally, the experiments are implemented using a redundant robot to validate the efficacy of the suggested approach with moving RCM constraints.

Identification of inverse kinematic parameters in redundant systems: Towards quantification of inter-joint coordination in the human upper extremity

Understanding and quantifying inter-joint coordination is valuable in several domains such as neurorehabilitation, robot-assisted therapy, robotic prosthetic arms, and control of supernumerary arms. Inter-joint coordination is often understood as a consistent spatiotemporal relation among kinematically redundant joints performing functional and goal-oriented movements. However, most approaches in the literature to investigate inter-joint coordination are limited to analysis of the end-point trajectory or correlation analysis of the joint rotations without considering the underlying task; e.g., creating a desirable hand movement toward a goal as in reaching motions. This work goes beyond this limitation by taking a model-based approach to quantifying inter-joint coordination. More specifically, we use the weighted pseudo-inverse of the Jacobian matrix and its associated null-space to explain the human kinematics in reaching tasks. We propose a novel algorithm to estimate such Inverse Kinematics weights from observed kinematic data. These estimated weights serve as a quantification for spatial inter-joint coordination; i.e., how costly a redundant joint is in its contribution to creating an end-effector velocity. We apply our estimation algorithm to datasets obtained from two different experiments. In the first experiment, the estimated Inverse Kinematics weights pinpoint how individuals change their Inverse Kinematics strategy when exposed to the viscous field wearing an exoskeleton. The second experiment shows how the resulting Inverse Kinematics weights can quantify a robotic prosthetic arm's contribution (or the level of assistance).

Fully Automated Bead Art Assembly for Smart Manufacturing Using Dynamic Compensation Approach

In this study, we demonstrate the implementation of make-to-order bead art assembly without human intervention using dynamic compensation approach to achieve accurate real-time positioning and long-term adaptation for robotic automation in smart manufacturing. In the proposed framework, an industrial robot was designed to perform coarse global motion to implement low-bandwidth adaptation. Simultaneously, fine local motion to tackle real-time online uncertainties was achieved using an add-on robotic module to implement accurate positioning. The effectiveness of the proposed method was verified through experimental evaluations.

Constraint-consistent task-oriented whole-body robot formulation: Task, posture, constraints, multiple contacts, and balance

We present a comprehensive formulation to the problem of controlling a high-dimensional robotic system involving complex tasks subject to a variety of constraints, obstacles, balance, and contact challenges. Using intuitive and natural representations, the approach is initiated by establishing individual objectives for a task and its constraints. Simple independent controllers using artificial potential fields are then designed for each objective to reach goals while enforcing the constraints. Dynamically consistent projections in nullspaces associated with task and constraint representations are employed to deliver a coherent whole-body robot control. In multi-link multi-contact tasks, contact forces produce both resulting and internal forces. Internal forces play a critical role in robot balance and stability, achieved in this framework through modeling and controlling virtual linkages that explicitly describe the relationship between active/passive contact force, resultant force, controlled/uncontrolled internal force for multi-link multi-contact underactuated robots. Control of contacts with the environment involves material considerations such as friction and geometric constraints. Potential barriers direct the selection of contact forces ensuring stability and balance. This approach of dynamic projection and the Virtual Linkage Model addresses robot underactuation. In addition, the framework introduces a coordinate completion mechanism to establish a generalized coordinates representation of the task, removing redundancy and maintaining the full operational space dynamics description. This enables task-space dynamic control based on the relevant inertial properties. We present the experimental validation on a physical humanoid platform.

A Robotic Experimental Setup with a Stewart Platform to Emulate Underwater Vehicle-Manipulator Systems

This study presents an experimental robotic setup with a Stewart platform and a robot manipulator to emulate an underwater vehicle-manipulator system (UVMS). This hardware-based emulator setup consists of a KUKA IIWA14 robotic manipulator mounted on a parallel manipulator, known as Stewart Platform, and a force/torque sensor attached to the end-effector of the robotic arm interacting with a pipe. In this setup, we use realistic underwater vehicle movements either communicated to a system in real-time through 4G routers or recorded in advance in a water tank environment. In addition, we simulate both the water current impact on vehicle movement and dynamic coupling effects between the vehicle and manipulator in a Gazebo-based software simulator and transfer these to the physical robotic experimental setup. Such a complete setup is useful to study the control techniques to be applied on the underwater robotic systems in a dry lab environment and allows us to carry out fast and numerous experiments, circumventing the difficulties with performing similar experiments and data collection with actual underwater vehicles in water tanks. Exemplary controller development studies are carried out for contact management of the UVMS using the experimental setup.

Modular representation and control of floppy networks

Geometric graph models of systems as diverse as proteins, DNA assemblies, architected materials and robot swarms are useful abstract representations of these objects that also unify ways to study their properties and control them in space and time. While much work has been done in the context of characterizing the behaviour of these networks close to critical points associated with bond and rigidity percolation, isostaticity, etc., much less is known about floppy, underconstrained networks that are far more common in nature and technology. Here, we combine geometric rigidity and algebraic sparsity to provide a framework for identifying the zero energy floppy modes via a representation that illuminates the underlying hierarchy and modularity of the network and thence the control of its nestedness and locality. Our framework allows us to demonstrate a range of applications of this approach that include robotic reaching tasks with motion primitives, and predicting the linear and nonlinear response of elastic networks based solely on infinitesimal rigidity and sparsity, which we test using physical experiments. Our approach is thus likely to be of use broadly in dissecting the geometrical properties of floppy networks using algebraic sparsity to optimize their function and performance.

Continuum Robots for Medical Interventions

Continuum robots are not constructed with discrete joints but, instead, change shape and position their tip by flexing along their entire length. Their narrow curvilinear shape makes them well suited to passing through body lumens, natural orifices, or small surgical incisions to perform minimally invasive procedures. Modeling and controlling these robots are, however, substantially more complex than traditional robots comprised of rigid links connected by discrete joints. Furthermore, there are many approaches to achieving robot flexure. Each presents its own design and modeling challenges, and to date, each has been pursued largely independently of the others. This article attempts to provide a unified summary of the state of the art of continuum robot architectures with respect to design for specific clinical applications. It also describes a unifying framework for modeling and controlling these systems while additionally explaining the elements unique to each architecture. The major research accomplishments are described for each topic and directions for the future progress needed to achieve widespread clinical use are identified.

Impedance Variation and Learning Strategies in Human-Robot Interaction

In this survey, various concepts and methodologies developed over the past two decades for varying and learning the impedance or admittance of robotic systems that physically interact with humans are explored. For this purpose, the assumptions and mathematical formulations for the online adjustment of impedance models and controllers for physical human-robot interaction (HRI) are categorized and compared. In this systematic review, studies on: 1) variation and 2) learning of appropriate impedance elements are taken into account. These strategies are classified and described in terms of their objectives, points of view (approaches), and signal requirements (including position, HRI force, and electromyography activity). Different methods involving linear/nonlinear analyses (e.g., optimal control design and nonlinear Lyapunov-based stability guarantee) and the Gaussian approximation algorithms (e.g., Gaussian mixture model-based and dynamic movement primitives-based strategies) are reviewed. Current challenges and research trends in physical HRI are finally discussed.

Using Deep Neural Networks to Improve Contact Wrench Estimation of Serial Robotic Manipulators in Static Tasks

Reliable force-driven robot-interaction requires precise contact wrench measurements. In most robot systems these measurements are severely incorrect and in most manipulation tasks expensive additional force sensors are installed. We follow a learning approach to train the dependencies between joint torques and end-effector contact wrenches. We used a redundant serial light-weight manipulator (KUKA iiwa 7 R800) with integrated force estimation based on the joint torques measured in each of the robot’s seven axes. Firstly, a simulated dataset is created to let a feed-forward net learn the relationship between end-effector contact wrenches and joint torques for a static case. Secondly, an extensive real training dataset was acquired with 330,000 randomized robot positions and end-effector contact wrenches and used for retraining the simulated trained feed-forward net. We can show that the wrench prediction error could be reduced by around 57% for the forces compared to the manufacturer’s proprietary force estimation model. In addition, we show that the number of high outliers can be reduced substantially. Furthermore we prove that the approach could be also transferred to another robot (KUKA iiwa 14 R820) with reasonable prediction accuracy and without the need of acquiring new robot specific data.

How to compete with robots by assessing job automation risks and resilient alternatives

The effects of robotics and artificial intelligence (AI) on the job market are matters of great social concern. Economists and technology experts are debating at what rate, and to what extent, technology could be used to replace humans in occupations, and what actions could mitigate the unemployment that would result. To this end, it is important to predict which jobs could be automated in the future and what workers could do to move to occupations at lower risk of automation. Here, we calculate the automation risk of almost 1000 existing occupations by quantitatively assessing to what extent robotics and AI abilities can replace human abilities required for those jobs. Furthermore, we introduce a method to find, for any occupation, alternatives that maximize the reduction in automation risk while minimizing the retraining effort. We apply the method to the U.S. workforce composition and show that it could substantially reduce the workers' automation risk, while the associated retraining effort would be moderate. Governments could use the proposed method to evaluate the unemployment risk of their populations and to adjust educational policies. Robotics companies could use it as a tool to better understand market needs, and members of the public could use it to identify the easiest route to reposition themselves on the job market.

Advanced Dynamics Processes Applied to an Articulated Robot

The paper presents the dynamics of a 2R planar articulated robot, developed by two original methods. One is the classical “Lagrangian” adapted by the author, and the second method is absolutely original. The dynamics of the robot are based in both cases on the variation of the inertial forces in the mechanism, or practically on the influence of the masses of the moving elements of the robot. The influence of external loads, weights and the load to be transported is also taken into account. Another original element of the work is the choice of speeds in such a way that they correspond to an optimum necessary for the inverse kinematics imposed on the robot. For this reason, the dynamic operation will be quiet and without large variations or vibrations. If the speeds of the two electric motors (preferably stepper motors) areadapted to those recommended by the author, the controller (PID) used will have a very light load. It is even possible to eliminate it if the adjustment of the two stepper motors (actuators) is performed according to the speeds indicated by the author of the paper. The kinematic motion imposed by the indicated optimal speeds is dynamically and successfully checked by both methods used.

Multi-Objective Optimal Torque Control with Simultaneous Motion and Force Tracking for Hydraulic Quadruped Robots

Model-based force control for motion and force tracking faces significant challenges on real quadruped platforms due to the apparent model inaccuracies. In this paper, we present a multi-objective optimal torque control for hydraulic quadruped robots under significant model errors, such as non-modelable hydraulic components, linearization, disturbances, etc. More specifically, the centroidal dynamics are first modeled to project the dynamics of the floating-based whole-body behaviors to the centroidal frame. Model error compensation mechanisms are subsequently developed to track the reference motion of the CoM, torso, and foot-end trajectories, which are mapped into the joint space. Furthermore, a multi-objective optimal torque control scheme is formulated using quadratic programming (QP) to coordinate follow the reference motion and ground reaction forces simultaneously while satisfying all constraints. Finally, we present a series of simulations as well as experiments on a real hydraulic quadruped platform, EHbot. The results demonstrate that the proposed torque control scheme is robust to large model inaccuracies and improves the performance of the overall system.

Locally active globally stable dynamical systems: Theory, learning, and experiments

State-dependent dynamical systems (DSs) offer adaptivity, reactivity, and robustness to perturbations in motion planning and physical human–robot interaction tasks. Learning DS-based motion plans from non-linear reference trajectories is an active research area in robotics. Most approaches focus on learning DSs that can (i) accurately mimic the demonstrated motion, while (ii) ensuring convergence to the target, i.e., they are globally asymptotically (or exponentially) stable. When subject to perturbations, a compliant robot guided with a DS will continue following the next integral curves of the DS towards the target. If the task requires the robot to track a specific reference trajectory, this approach will fail. To alleviate this shortcoming, we propose the locally active globally stable DS (LAGS-DS), a novel DS formulation that provides both global convergence and stiffness-like symmetric attraction behaviors around a reference trajectory in regions of the state space where trajectory tracking is important. This allows for a unified approach towards motion and impedance encoding in a single DS-based motion model, i.e., stiffness is embedded in the DS. To learn LAGS-DS from demonstrations we propose a learning strategy based on Bayesian non-parametric Gaussian mixture models, Gaussian processes, and a sequence of constrained optimization problems that ensure estimation of stable DS parameters via Lyapunov theory. We experimentally validated LAGS-DS on writing tasks with a KUKA LWR 4+ arm and on navigation and co-manipulation tasks with iCub humanoid robots.

Whole-Body Teleoperation and Shared Control of Redundant Robots with Applications to Aerial Manipulation

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.

Analysis of Methods for Incremental Policy Refinement by Kinesthetic Guidance

Traditional robot programming is often not feasible in small-batch production, as it is time-consuming, inefficient, and expensive. To shorten the time necessary to deploy robot tasks, we need appropriate tools to enable efficient reuse of existing robot control policies. Incremental Learning from Demonstration (iLfD) and reversible Dynamic Movement Primitives (DMP) provide a framework for efficient policy demonstration and adaptation. In this paper, we extend our previously proposed framework with improvements that provide better performance and lower the algorithm’s computational burden. Further, we analyse the learning stability and evaluate the proposed framework with a comprehensive user study. The proposed methods have been evaluated on two popular collaborative robots, Franka Emika Panda and Universal Robot UR10.

Biped Walking Based on Stiffness Optimization and Hierarchical Quadratic Programming

The spring-loaded inverted pendulum model is similar to human walking in terms of the center of mass (CoM) trajectory and the ground reaction force. It is thus widely used in humanoid robot motion planning. A method that uses a velocity feedback controller to adjust the landing point of a robot leg is inaccurate in the presence of disturbances and a nonlinear optimization method with multiple variables is complicated and thus unsuitable for real-time control. In this paper, to achieve real-time optimization, a CoM-velocity feedback controller is used to calculate the virtual landing point. We construct a touchdown return map based on a virtual landing point and use nonlinear least squares to optimize spring stiffness. For robot whole-body control, hierarchical quadratic programming optimization is used to achieve strict task priority. The dynamic equation is given the highest priority and inverse dynamics are directly used to solve it, reducing the number of optimizations. Simulation and experimental results show that a force-controlled biped robot with the proposed method can stably walk on unknown uneven ground with a maximum obstacle height of 5 cm. The robot can recover from a 5 Nm disturbance during walking without falling.