This paper addresses the multi-faceted problem of robot grasping, where multiple criteria may conflict and differ in importance. We introduce Grasp Ranking and Criteria Evaluation (GRaCE), a novel approach that employs hierarchical rule-based logic and a rank-preserving utility function to optimize grasps based on various criteria such as stability, kinematic constraints, and goal-oriented functionalities. Additionally, we propose GRaCE-OPT, a hybrid optimization strategy that combines gradient-based and gradient-free methods to effectively navigate the complex, non-convex utility function. Experimental results in both simulated and real-world scenarios show that GRaCE requires fewer samples to achieve comparable or superior performance relative to existing methods. The modular architecture of GRaCE allows for easy customization and adaptation to specific application needs.

Abstract-We present an approach to control a 6 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 article is the definition of a new virtual visual space (image space). A novel stereo camera model employing virtual orthogonal cameras is used to map 6D poses from Cartesian space to this virtual visual space. Each component of the 6D 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 are 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.

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.

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Spiking neural networks (SNNs) offer many advantages over traditional artificial neural networks (ANNs) such as biological plausibility, fast information processing, and energy efficiency. Although SNNs have been used to solve a variety of control tasks using the Spike-Timing-Dependent Plasticity (STDP) learning rule, existing solutions usually involve hard-coded network architectures solving specific tasks rather than solving different kinds of tasks generally. This results in neglecting one of the biggest advantages of ANNs, i.e., being general-purpose and easy-to-use due to their simple network architecture, which usually consists of an input layer, one or multiple hidden layers and an output layer. This paper addresses the problem by introducing an end-to-end learning approach of spiking neural networks constructed with one hidden layer and reward-modulated Spike-Timing-Dependent Plasticity (R-STDP) synapses in an all-to-all fashion. We use the supervised reward-modulated Spike-Timing-Dependent-Plasticity learning rule to train two different SNN-based sub-controllers to replicate a desired obstacle avoiding and goal approaching behavior, provided by pre-generated datasets. Together they make up a target-reaching controller, which is used to control a simulated mobile robot to reach a target area while avoiding obstacles in its path. We demonstrate the performance and effectiveness of our trained SNNs to achieve target reaching tasks in different unknown scenarios.

In this paper, we present an uncalibrated positionbased 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. Distributed System Visual Stereo Tracker Robot Low Level Control 3D Visualization System 2 cameras [Uncalibrated Stereo System] {30ms} t or q d Voltage q,q q,q Virtual Obstacle