We propose a new approach to programming by the demonstration of finishing operations. Such operations can be carried out by industrial robots in multiple ways because an industrial robot is typically functionally redundant with respect to a finishing task. In the proposed system, a human expert demonstrates a finishing operation, and the demonstrated motion is recorded in the Cartesian space. The robot’s kinematic model is augmented with a virtual mechanism, which is defined according to the applied finishing tool. This way, the kinematic model is expanded with additional degrees of freedom that can be exploited to compute the optimal joint space motion of the robot without altering the essential aspects of the Cartesian space task execution as demonstrated by the human expert. Finishing operations, such as polishing and grinding, occur in contact with the treated workpiece. Since information about the contact point position is needed to control the robot during the operation, we have developed a novel approach for accurate estimation of contact points using the measured forces and torques. Finally, we applied iterative learning control to refine the demonstrated operations and compensate for inaccurate calibration and different dynamics of the robot and human demonstrator. The proposed method was verified on real robots and real polishing and grinding tasks.Note to Practitioners—This work was motivated by the need for automation of finishing operations, such as polishing and grinding, on contemporary industrial robots. Existing approaches are both too complex and too time-consuming to be applied in flexible and small-scale production, which often requires the frequent deployment of new applications. Our approach is based on programming by demonstration and enables the programming of finishing operations also for users who are not specialists in robot programming. Programming by demonstration is especially useful for teaching finishing operations because it enables the transfer of expert knowledge about finishing skills to robots without providing lengthy task descriptions or manual coding. Besides the human demonstration of the desired operation, the proposed approach also requires the availability of the kinematic model for the machine tool applied to carry out the finishing operation. We provide several practical examples of grinding and polishing tools and how to integrate them into our approach. Another feature of the proposed system is that user demonstrations of finishing operations can be transferred between different combinations of robots and machine tools.

Assembly tasks performed with a robot often fail due to unforeseen situations, regardless of the fact that we carefully learned and optimized the assembly policy. This problem is even more present in humanoid robots acting in an unstructured environment where it is not possible to anticipate all factors that might lead to the failure of the given task. In this work, we propose a concurrent LfD framework, which associates demonstrated exception strategies to the given context. Whenever a failure occurs, the proposed algorithm generalizes past experience regarding the current context and generates an appropriate policy that solves the assembly issue. For this purpose, we applied PCA on force/torque data, which generates low dimensional descriptor of the current context. The proposed framework was validated in a peg-in-hole (PiH) task using Franka-Emika Panda robot.

In this paper we propose a new approach for efficient programming of grinding and polishing operation. In the proposed system, the initial policy is performed by a skilled operator and recorded with a passive digitizer. The demonstrated policy comprises both position and force data. The optimal robot execution of the task is provided by applying a virtual mechanism approach, which models the polishing/grinding tool as a serial kinematic chain. By joining the robot and the virtual mechanism in an augmented system, additional degrees of freedom are obtained and redundancy resolution can be applied to optimize the demonstrated motion. Another benefit of the proposed approach is that the same policy can be transferred to different combination of robots and grinding/polishing tools without any modification of the captured motion. The proposed approach requires known contact point between the treated object and the polishing/grinding tool. We propose a novel approach for accurate estimation of this point using data obtained from the force-torque sensor. Finally, the demonstrated path is refined to compensate for inaccurate calibration and different dynamics of a robot and the human demonstrator using iterative learning controller. The proposed method was verified in a real industrial environment.

Encoding desired motions into dynamic movement primitives (DMPs) is a common way for generating compact task representations that are able to handle sensor-based goal adaptations. At the same time, a robot should not only express adaptive motion capabilities at planning level, but use also contact wrench feedback in the adaptation and learning process of the DMP. Despite first approaches exist in this direction, no fully integrated approach has been proposed so far. In this paper, we introduce a new class of admittance-coupled DMPs that addresses environmental changes by including contact wrench feedback dynamics into the DMP formalism. Moreover, a novel iterative learning approach is devised that is based on monitoring the overall system passivity analysis in terms of reference power tracking. Simulations and experimental results with the Kuka LWR robot maintaining a non-rigid contact with the environment (wiping a surface) are shown for supporting the validity of our approach.

We present a three-level cognitive system in a learning by demonstration context. The system allows for learning and transfer on the sensorimotor level as well as the planning level. The fundamentally different data structures associated with these two levels are connected by an efficient mid-level representation based on so-called “semantic event chains.” We describe details of the representations and quantify the effect of the associated learning procedures for each level under different amounts of noise. Moreover, we demonstrate the performance of the overall system by three demonstrations that have been performed at a project review. The described system has a technical readiness level (TRL) of 4, which in an ongoing follow-up project will be raised to TRL 6.

In this study we propose a new method to enhance the performance of iterative learning control (ILC). We focus on robotic tasks dealing with adaptation to the unknown or partially known environment, where the robot has to learn the environment geometry in order to perform the desired task with the given reference forces and torques. The initial motion trajectories are obtained by kinesthetic teaching, whereas the required forces and torques are prescribed by the task. We are interested in incremental learning, which assures smooth and safe operation, aiming at handling of delicate, fragile objects, such as objects made of glass. In order to achieve these goals we propose a new adaptive ILC scheme, where the adaptation is supervised by reinforcement learning. We also show how to apply ILC to orientational motion, taking into account the curved geometry of SO(3). The performance of the proposed algorithm is verified on a bi-manual glass wiping task.

An effective robot trajectory representation should encode all relevant aspects of the desired motion. For kinematic representations, this means that both the spatial course of the trajectory and its speed profile must be specified. The concept of dynamic movement primitives (DMP) provides a kinematic representation that fully specifies these two aspects of motion. They are, however, not separated from each other within the DMP representation. This can be problematic when movements with significant speed variations are compared within movement recognition and skill learning algorithms. In such comparisons it is often important to distinguish between the spatial and temporal aspects of motion. In this paper we propose a new representation based on dynamic movement primitives, where spatial and temporal aspects are well separated. We demonstrate the effectiveness of the proposed representation for statistical learning of robot skills and movement recognition and compare the performance with standard DMPs, where temporal and spatial aspects of motion are intertwined.

In this paper we exploit Iterative Learning Controllers (ILC) schemes in force adaptation tasks. We propose to encode the control signal with Radial Basis Functions (RBF), which enhances the robustness of the ILC scheme and allows to vary the execution speed of the learned motion. For that a novel control scheme is proposed, which updates the feedforward compensation signals based on current iteration cycle signals in contrast to the standard ILC, which uses signals from the previous iteration cycle. This reduces the computational burden and enhances the adaptation speed. Stability of the proposed control law is analysed and discussed. The proposed approach is evaluated in simulation and on a Kuka Light Weight Robot Arm where the task was to perform force-based surface following with both discrete and periodic movements.

Dynamic movement primitives (DMPs) were proposed as an efficient way for learning and control of complex robot behaviors. They can be used to represent point-to-point and periodic movements and can be applied in Cartesian or in joint space. One problem that arises when DMPs are used to define control policies in Cartesian space is that there exists no minimal, singularity-free representation of orientation. In this paper we show how dynamic movement primitives can be defined for non minimal, singularity free representations of orientation, such as rotation matrices and quaternions. All of the advantages of DMPs, including ease of learning, the ability to include coupling terms, and scale and temporal invariance, can be adopted in our formulation. We have also proposed a new phase stopping mechanism to ensure full movement reproduction in case of perturbations.

The framework of dynamic movement primitives (DMPs) contains many favorable properties for the execution of robotic trajectories, such as indirect dependence on time, response to perturbations, and the ability to easily modulate the given trajectories, but the framework in its original form remains constrained to the kinematic aspect of the movement. In this paper, we bridge the gap to dynamic behavior by extending the framework with force/torque feedback. We propose and evaluate a modulation approach that allows interaction with objects and the environment. Through the proposed coupling of originally independent robotic trajectories, the approach also enables the execution of bimanual and tightly coupled cooperative tasks. We apply an iterative learning control algorithm to learn a coupling term, which is applied to the original trajectory in a feed-forward fashion and, thus, modifies the trajectory in accordance to the desired positions or external forces. A stability analysis and results of simulated and real-world experiments using two KUKA LWR arms for bimanual tasks and interaction with the environment are presented. By expanding on the framework of DMPs, we keep all the favorable properties, which is demonstrated with temporal modulation and in a two-agent obstacle avoidance task.

The framework of dynamic movement primitives allows the generation of discrete and periodic trajectories, which can be modulated in various aspects. We propose and evaluate a novel modulation approach that includes force feedback and thus allows physical interaction with objects and the environment. The proposed approach also enables the coupling of independently executed robotic trajectories, simplifying the execution of bimanual and tightly coupled cooperative tasks. We apply an iterative learning control algorithm to learn a coupling term, which is applied to the original trajectory in a feed-forward fashion. The coupling term modifies the trajectory in accordance to either the desired position or external force. The strengths of the approach are shown in bimanual or two-agent obstacle avoidance tasks, where no higher level cognitive reasoning or planning are required. Results of simulated and real-world experiments on the ARMAR-III humanoid robot in interaction and object lifting tasks, and on two KUKA LWR robots in a bimanual setting are presented.

In this paper we propose a novel method for ensuring stability of a skiing robot on a previously unknown ski slope. The method is based on a null-space velocity control algorithm, which is essential for achieving good behavior of redundant robotic systems. Using the pseudo-inverse, the task and the null-space motions are decoupled. For the skiing robot, the primary task is stability, the secondary task is the direction and the third task is the pose of the robot. The dynamic stability index of the skiing robot is defined with the normalized zero moment point. We demonstrated the effectiveness of the proposed method both in simulation in the virtual reality environment as well as on the real robot.

The paper describes the control and the navigation of a skiing robot that is capable of autonomous skiing on a ski slope using the carving skiing technique. Based on a complex sensory system it is capable of autonomous navigating between the race gates, avoiding obstacles and maintaining a stable position during skiing on an previously unknown ski slope. The robot was tested on different ski slopes with various race gates combination.

In the paper we address a problem of controlling an oscillating motion with a robot. As the object we have selected a yo-yo. Yo-yo is a toy made of two discs connected with a short thin axle, which can be moved up and down by moving a string tied to the axle. Although it is very easy to play with yo-yo for a human, designing a control system for a robot is quite a challenge. We present a simplified model of a yo-yo, which is intended for control analysis and design. Our model has one degree-of-freedom and the behaviour at the end of the string is modelled as an impact. Next we discuss the control strategy and present a control method, which allows playing the yo-yo at a selected top height.

The paper deals with kinematic control algorithms for on-line obstacle avoidance which allow a kinematically redundant manipulator to move in an unstructured environment without colliding with obstacles. The presented approach is based on the redundancy resolution at the velocity level. The primary task is determined by the end-effector trajectories and for obstacle avoidance the internal motion of the manipulator is used. The obstacle avoiding motion is defined in one-dimensional operational space and, hence, the system has less singularities making implementation easier. Instead of the exact pseudoinverse solution we propose an approximate one which is computationally more efficient and allows us to consider many simultaneously active obstacles without any problems. The fast cycle times of the numerical implementation enable use of the algorithm in real-time control. For illustration, some simulation results of a highly redundant planar manipulator moving in an unstructured and time-varying environment and experimental results of a four link planar manipulator are given.

We deal with implementation of force control on a planar 4 DOF experimental redundant robot. A globally stable impedance force control law is used to control force and trajectory in the operational space. Redundancy is solved by a gradient optimization technique using dynamically consistent pseudo-inverse. In our case we implemented two optimization criteria-obstacle avoidance and torque optimization, important for force controlled redundant robots. The desired null space motion is controlled with a new formulation of the null space velocity controller, that assures globally stable behavior in null space. The performance of the proposed control law was tested on the experimental 4 DOF planar manipulator.

The authors deal with the implementation of adaptive control of force-controlled manipulators. Due to the hardware and software limitations of existing industrial robots, the force control loop must be realized in many cases with an inner velocity and position controller. In such cases, the response of the robot becomes sensitive to the changes of the environment stiffness. To solve the problem, an adaptive controller has been proposed by many authors. The main drawback of these controllers is that they cannot assure satisfactory behavior in the case when both environment stiffness and environment/robot contact position are changed simultaneously. Therefore a dual force sensor is proposed that is passive and active. which allows estimation of the contact position and the environment stiffness simultaneously. Adaptive force control was tested on the deburring process of a workpiece.>

A closed-loop algorithm for solving the inverse kinematics problem is presented. An easy-to-implement method achieves coordinate transformation using the sliding-mode principle and a Lyapunov-like concept. An attractive feature of this algorithm over the Newton-Raphson method is that its computational burden is reduced. Furthermore, it requires no Jacobian matrix inversion and thus avoids the numerical instabilities associated with matrix singularities. Simulation results demonstrate the performance of the proposed algorithm for a robot that has no analytical solution to the inverse kinematics problem.>