In this paper we present a framework to learn skills from human demonstrations in the form of geometric nullspaces, which can be executed using a robot. We collect data of human demonstrations, fit geometric nullspaces to them, and also infer their corresponding geometric constraint models. These geometric constraints provide a powerful mathematical model as well as an intuitive representation of the skill in terms of the involved objects. To execute the skill using a robot, we combine this geometric skill description with the robot's kinematics and other environmental constraints, from which poses can be sampled for the robot's execution. The result of our framework is a system that takes the human demonstrations as input, learns the underlying skill model, and executes the learnt skill with different robots in different dynamic environments. We evaluate our approach on a simulated industrial robot, and execute the final task on the iCub humanoid robot.

In this study, the spectral efficiency (SE) and energy efficiency (EE) tradeoff in full‐duplex (FD) two‐hop decode‐and‐forward (DF) relaying system is studied by considering the influence of residual self‐interference. In order to increase the SE of the traditional DF relaying system, firstly, an optimal power allocation (OPA) method is proposed, and the SE and EE analyses of the system are obtained. Secondly, by applying an unified SE–EE tradeoff metric, the multi‐object joint SE and EE maximisation problem is formulated as a single‐objective joint maximisation problem for SE and EE. Furthermore, the optimal total transmission power of the single‐objective joint SE and EE maximisation problem is obtained according to the tradeoff factors. Theoretical analysis and numerical results show that the proposed OPA method significantly improve the system's SE and EE. At the same time, it reveals that the tradeoff between the SE and EE can be flexible by setting the tradeoff factors.

This paper introduces an end-to-end learning approach based on Reward-modulated Spike-Timing-Dependent Plasticity (R-STDP) for a multi-layered spiking neural network (SNN). As a case study, a snake-like robot is used as an agent to perform target tracking tasks on the basis of our proposed approach. Since the key of R-STDP is to use rewards to modulate synapse strengthens, we first propose a general way to propagate the reward back through a multi-layered SNN. Upon the proposed approach, we build up an SNN controller that drives a snake-like robot for performing target tracking tasks. We demonstrate the practicability and advantage of our approach in terms of lateral tracking accuracy by comparing it to other state-of-the-art learning algorithms for SNNs based on R-STDP.

The sense of touch is arguably the first human sense to develop. Empowering robots with the sense of touch may augment their understanding of interacted objects and the environment beyond standard sensory modalities (e.g., vision). This paper investigates the effect of hybridizing touch and sliding movements for tactile-based texture classification. We develop three machine-learning methods within a framework to discriminate between surface textures; the first two methods use hand-engineered features, whilst the third leverages convolutional and recurrent neural network layers to learn feature representations from raw data. To compare these methods, we constructed a dataset comprising tactile data from 23 textures gathered using the iCub platform under a loosely constrained setup, i.e., with nonlinear motion. In line with findings from neuroscience, our experiments show that a good initial estimate can be obtained via touch data, which can be further refined via sliding; combining both touch and sliding data results in 98% classification accuracy over unseen test data.

In this paper, we show that visual servoing tasks can be integrated into a prioritized constraint-based torque control framework. This framework can achieve real time control of robots performing strict prioritized motion and forces tasks. A null-space projector is used to combine the tasks with different priorities. The visual servoing is considered just another task formulated with constraints in the whole body control framework. Several relevant constraints (i.e., motion constraints, joint limits) are tested to evaluate the control framework. Further, we evaluate the proposed approach in typical industrial robotics applications: grasping of cylindrical objects, and two position/force control applications (Erasing and Peg-in-Hole).

In this paper, we propose a framework for prioritized constraint-based specification of robot tasks. This framework is integrated with a cognitive robotic system based on semantic models of processes, objects, and workcells. The target is to enable intuitive (re-)programming of robot tasks, in a way that is suitable for non-expert users typically found in SMEs. Using CAD semantics, robot tasks are specified as geometric inter-relational constraints. During execution, these are combined with constraints from the environment and the workcell, and solved in real-time. Our constraint model and solving approach supports a variety of constraint functions that can be non-linear and also include bounds in the form of inequalities, e.g., geometric inter-relations, distance, collision avoidance and posture constraints. It is a hierarchical approach where priority levels can be specified for the constraints, and the nullspace of higher priority constraints is exploited to optimize the lower priority constraints. The presented approach has been applied to several typical industrial robotic use-cases to highlight its advantages compared to other state-of-the-art approaches.

It has been shown that amplify‐and‐forward (AF) transmission is a viable transmission protocol for wireless networks incorporating distributed spatial diversity. However, a drawback of this relatively simple protocol is identified, the destination node is only to combine a part of the signal, while the other part of the signal has been regarded as interference for delay, leading to the lower utilisation of the resource inevitably, which manifests in the bigger noise, lower signal‐to‐noise ratio, and higher bit error rate. A revised AF relay protocol has been proposed for it could fully combine signal and eliminate delay at destination node through caching signal. A multi‐user single‐relay transmission model could demonstrate the superior performance of this protocol, which is in a hotspots region of a cognitive radio network. The closed‐form solution of the channel transmission rate is obtained with the form of matrix multiplication, and it can ensure that even if without any interference cancellation operation it still can improve the transmission rate. Analytical expression of downlink transmission energy efficiency (DLEE) has been derived. Numerical simulation results show that the transmission performances and DLEE have been improved by proposed protocol compared with the conventional AF relay protocol and direct transmission.

We present an approach to control a 6-degree-of-freedom (DOF) manipulator using an uncalibrated visual servoing (VS) approach that addresses the challenges of choosing proper image features for target objects and designing a VS controller to enhance the tracking performance. The main contribution of this paper is the definition of a new virtual visual space (image space). A novel stereo camera model employing virtual orthogonal cameras is used to map 6-D poses from Cartesian space to this virtual visual space. Each component of the 6-D pose vector defined in this virtual visual space is linearly independent, leading to a full-rank 6 × 6 image Jacobian matrix, which allows avoiding classical problems, such as image space singularities and local minima. Furthermore, the control for rotational and translational motion of robot is decoupled due to the diagonal image Jacobian. Finally, simulation results with an eye-to-hand robotic system confirm the improvement in controller stability and motion performance with respect to conventional VS approaches. Experimental results on a 6-DOF industrial robot are provided to illustrate the effectiveness of the proposed method and the feasibility of using this method in practical scenarios.

In this paper, we present an uncalibrated position-based fixed-camera Visual Servoing for robot manipulators, where the goal is to track the 3D position and orientation of the target. The stereo system with 2 USB cameras is uncalibrated with respect to the robot base frame and the transformation between them is estimated on-line while performing the task. Dynamic impedance control is designed to generate a dynamic trajectory for the robot manipulator considering the dynamic environment constraints, such as: robot singularities avoidance and (self-/obstacle-) collision avoidance. Experiments have been carried out to verify performance of the proposed system on a real industrial robot, where the calibration estimation process and handling of all uncertainties in the environment are demonstrated. Moreover the uncalibrated stereo camera system can be manually moved while performing the task in order to obtain a clearer view and the re-calibration is performed automatically and on-line.

This paper introduces 6 new image features to provide a solution to the open problem of uncalibrated 6D image-based visual servoing for robot manipulators, where the goal is to control the 3D position and orientation of the robot end-effector using visual feedback. One of the main contributions of this article is a novel stereo camera model which employs virtual orthogonal cameras to map 6D Cartesian poses defined in the Task space to 6D visual poses defined in a Virtual Visual space (Image space). This new model is used to compute a full-rank square Image Jacobian matrix (Jimg), which solves several common problems exhibited by the classical image Jacobians, e.g., Image space singularities and local minima. This Jacobian is a fundamental key for the image-based controller design, where a chattering-free adaptive second order sliding mode is employed to track 6D visual motions for a robot manipulator. Exponential convergence of errors in both spaces without local minima are demonstrated. The complete control system is experimentally evaluated on a real industrial robot. The robustness of the control scheme is evaluated for cases where the extrinsic parameters of the uncalibrated stereo camera system are changed on-line and unknown when the stereo system is manually moved to obtain a clearer view of the task.

This paper introduces a new comprehensive solution for the open problem of uncalibrated 3D image-based stereo visual servoing for robot manipulators. One of the main contributions of this article is a novel 3D stereo camera model to map positions in the task space to positions in a new 3D Visual Cartesian Space (a visual feature space where 3D positions are measured in pixel). This model is used to compute a full-rank Image Jacobian Matrix (Jimg), which solves several common problems presented on the classical image Jacobians, e.g., image space singularities and local minima. This Jacobian is a fundamental key for the image-based control design, where uncalibrated stereo camera systems can be used to drive a robot manipulator. Furthermore, an adaptive second order sliding mode visual servo control is designed to track 3D visual motions using the 3D trajectory errors defined in the Visual Cartesian Space. The stability of the control in closed loop with a dynamic robot system is formally analyzed and proved, where exponential convergence of errors in the Visual Cartesian Space and task space without local minima are demonstrated. The complete control system is evaluated both in simulation and on a real industrial robot. The robustness of the control scheme is evaluated for cases where the extrinsic parameters of the stereo camera system change on-line and the kinematic/dynamic robot parameters are considered as unknown. This approach offers a proper solution for the common problem of visual occlusion, since the stereo system can be moved to obtain a clear view of the task at any time.