Neuro-Genetic Visuomotor Architecture for Robotic Grasping

In this article we present a technique that enhances an inverse kinematics (IK) solver such that when the results are applied to a computer character, we can generate a level of individualization tailored to both the character and the environment, e.g., a walking motion can become "stiffer" or can be turned into a limping motion. Since the technique is based on an IK solver, we also have the desirable effect of solving retargetting issues when mapping motion data between characters. As the individualization aspect of our technique is very tightly coupled with the inverse kinematics solver, we can achieve both the individualization and retargetting of characters in real time.

As humanoid robotics advances beyond bipedal walking, complex motions involving the whole body are necessary. Most recent humanoids represent the range of motion of the human spine with a single rotation joint. While this joint allows the body to swing during dynamic walking, any bending must be performed only with the legs. This paper develops a skewed rotation plane (SRP) waist joint to give a humanoid robot the same range of torso motion as a human. The SRP design reduces holding torque compared to a orthogonal-axis joint. An inverse kinematics solver using Jacobian Pseudo-inverse was developed to produce smooth torso orientation trajectories. Finally, a mechanical prototype developed and fitted to Drexel University's Jaemi Hubo to verify and validate the model.

In order to grasp an object, we need to solve the inverse kinematics problem, i.e., the coordinate transformation from the visual coordinates to the joint angle vector coordinates of the arm. In human motion control, the learning of the hand position error feedback controller in the inverse kinematics solver is important. Although several models of coordinate transformation learning have been proposed, they suffer from a number of drawbacks. This paper proposes a novel model of the coordinate transformation learning of the human visual feedback controller by using disturbance noise and feedback error signal. The feasibility of the proposed model is illustrated using numerical simulations.

This paper presents a technique for adapting existing motion of a human-like character to have the desired features that are specified by a set of constraints. This problem can be typically formulated as a spacetime constraint problem. Our approach combines a hierarchical curve fitting technique with a new inverse kinematics solver. Using the kinematics solver, we can adjust the configuration of an articulated figure to meet the constraints in each frame. Through the fitting technique, the motion displacement of every joint at each constrained frame is interpolated and thus smoothly propagated to frames. We are able to adaptively add motion details to satisfy the constraints within a specified tolerance by adopting a multilevel Bspline representation which also provides a speedup for the interpolation. The performance of our system is further enhanced by the new inverse kinematics solver. We present a closed-form solution to compute the joint angles of a limb linkage. This analytical method greatly reduces the burden of a numerical optimization to find the solutions for full degrees of freedom of a human-like articulated figure. We demonstrate that the technique can be used for retargetting a motion to compensate for geometric variations caused by both characters and environments. Furthermore, we can also use this technique for directly manipulating a motion clip through a graphical interface. CR Categories: I.3.7 [Computer Graphics]: Threedimensional Graphics—Animation; G.1.2 [Numerical Analysis]: Approximation—Spline and piecewise polynomial approximation

We present TrueRMA, a data-efficient, model-free method to learn cost-optimized robot trajectories over a wide range of starting points and endpoints. The key idea is to calculate trajectory waypoints in Cartesian space by recursively predicting orthogonal adaptations relative to the midpoints of straight lines. We generate a differentiable path by adding circular blends around the waypoints, calculate the corresponding joint positions with an inverse kinematics solver and calculate a time-optimal parameterization considering velocity and acceleration limits. During training, the trajectory is executed in a physics simulator and costs are assigned according to a user-specified cost function which is not required to be differentiable. Given a starting point and an endpoint as input, a neural network is trained to predict midpoint adaptations that minimize the cost of the resulting trajectory via reinforcement learning. We successfully train a KUKA iiwa robot to keep a ball on a plate while moving between specified points and compare the performance of TrueRMA against two baselines. The results show that our method requires less training data to learn the task while generating shorter and faster trajectories.

Robotic manipulation in surgical applications often demands the surgical instrument to pivot around a fixed point, known as remote center of motion (RCM). The RCM constraint ensures that the pivot point of the surgical tool remains stationary at the incision port, preventing tissue damage and bleeding. Precisely and efficiently controlling tool positioning and orientation under this constraint poses a complex Inverse Kinematics (IK) problem that must be solved in real-time to ensure patient safety and minimize complications. To address this problem, we propose PivotIK, an efficient IK solver that combines efficient evolutionary exploration with multi-objective Jacobian-based optimization. PivotIK can track desired tool poses accurately while satisfying the RCM constraint in real-time. We evaluated PivotIK through simulations and real-world experiments using redundant robotic manipulators with multi-degree-of-freedom surgical instruments. We compare PivotIK with other IK solvers in terms of solve rates, computation times, tracking errors, and RCM errors under various scenarios, including unconstrained and RCM-constrained trajectories. Our results show that PivotIK achieves superior performance, solving the IK problem in less than 1 ms with errors below 0.01 and 0.1 mm for tracking and RCM, respectively. Our real-world experiments confirm the effectiveness of PivotIK in ensuring smooth trajectory tracking and RCM compliance. PivotIK offers a promising solution for real-time IK for robotic manipulation under RCM constraints in surgical applications.

In the framework of virtual prototyping, a virtual human is a useful tool to evaluate what one can reach or see. However because engineers and industrial designers are not necessarily skilled animators, it is important to provide intuitive or high-level interfaces to control virtual characters. With those considerations in mind, in this thesis we investigate three interfaces to control virtual humans for reaching tasks. First we propose an online full-body control using motion capture technology. The posture is reconstructed with an inverse kinematics solver. Coupled with a collision avoidance module, it is a powerful interface to control interactively virtual humans in cluttered environment. Secondly, we propose a novel interface for the full-body online control of differing-height characters. Instead of scaling the markers to the size of the virtual human, we suggest inversely scaling the virtual world. We conduct a user-study to evaluate if such interface is susceptible to help the user experiences the efforts involved by the differing-height character to complete a reaching task. Finally, we investigate motion graph technology for navigation coupled with reaching motions.

In recent years, hydraulic manipulators have been more and more widely used in the field of industrial automation because of their high power-to-weight ratio. Compared with autonomous control, bilateral teleoperation is a more reliable way to operate hydraulic manipulators on harsh occasions. However, teleoperation application in the hydraulic field is still limited due to operation accuracy and master–slave heterogeneous problem. In this paper, a teleoperation framework based on master–slave heterogeneous matching and nonlinear precise motion control for hydraulic manipulator is proposed, including workspace mapping algorithm, velocity-limited interpolator, inverse kinematics solver, trajectory planner, model-based controller, and feedback torque generator. The high-precision teleoperation of the hydraulic manipulator in the situation of master–slave heterogeneity is finally realized based on the designed teleoperation framework. The specialized experiment is set up, and the results also show that the method proposed in this paper can satisfy the teleoperation requirements of the hydraulic manipulator with the maximum operating accuracy up to 0.02 m under the condition of master–slave heterogeneity, which can greatly improve the applicability of hydraulic manipulator teleoperation in the case of master–slave heterogeneity.

Imitation learning is a way to teach robots skills that are demonstrated by humans. Transfering skills between these different kinematic structures seems to be straightforward in Cartesian space. Because of the correspondence problem, however, the result will most likely not be identical. This is why refinement is required, for example, by policy search. Policy search in Cartesian space is prone to reachability problems when using conventional inverse kinematic solvers. We propose a configurable approximate inverse kinematic solver and show that it can accelerate the refinement process considerably. We also compare empirically refinement in Cartesian space and refinement in joint space.

Robotic assistance in nursing is becoming increasingly important due to labor shortage and novel capabilities in hard- and software. In nursing, patients often need to be manipulated which requires force distribution beyond the scope of traditional end effectors. This can be solved with whole arm manipulation (WAM). Unfortunately, commanding WAM configurations for grasping patients is very complex, as many contact points need to be achieved, and all 6 or 7 degrees of freedom have to be defined. In this paper, a novel interaction concept and according to inverse kinematic solvers based on a local elliptic approximation are proposed, which allows commanding a WAM holding grasp with one click on a point cloud. It is shown how the local elliptic model is used for formulating a very fast analytic multi-contact inverse kinematic solver. Two alternative solvers are derived by extending the BioIK method and by utilizing the popular sequential quadratic programming approach. Experiments show the efficiency of the analytic method and the robustness of the local elliptic model relative to perceptual perturbations.

Articulated hand tracking systems have been commonly used in virtual reality applications, including systems with human-computer interaction or interaction with game consoles. However, building an effective real-time hand pose tracker remains challenging. In this paper, we present a simple and efficient methodology for tracking and reconstructing 3d hand poses using a markered optical motion capture system. Markers were positioned at strategic points, and an inverse kinematics solver was incorporated to fit the rest of the joints to the hand model. The model is highly constrained with rotational and orientational constraints, allowing motion only within a feasible set. The method is real-time implementable and the results are promising, even with a low frame rate.

Following the vision of Industry 4.0, manufacturers started to break down products into subsystems, each of which is developed and supplied by different suppliers to tackle the increasing complexity of product development (e.g., in vehicle, machine or plant construction). This process needs to be supported by intuitive Computer-Aided Design (CAD) tools and virtual modeling and simulation environments to effectively meet the challenges of increasingly complex product development. Therefore, in this paper, we present some outcoming of a research project called DisMoSim ( Dis tributed Mo deling and Sim ulation), which proposes an intuitive virtual environment for modeling and simulation that embeds a Multi User Inverse Kinematic Solver (MUKS). The MUKS allows (i) individual subsystems to be tested intuitively, i.e., Rapid Testing (RT), and (ii) people not yet familiar with a subsystem to understand it easily and quickly through simple kinematic actions, i.e., Rapid Understanding (RU), without unnaturally interrupting the modeling process. Finally, (iii) the MUKS enables subsystems to be easily assembled into complete systems, i.e., Rapid Assembling (RA), by means of a so-called snapping-point mechanism which supports the complex process of aligning and assembling different subsystems. To evaluate these capabilities, we present the results of a user study conducted with 20 participants. Special attention has been given to the embedded MUKS in contrast to existing approaches known from common CAD and simulation software. Thus, we demonstrate the practicability, usability, and versatility of our approach. • Finding modeling and simulation errors in CAD models is cumbersome with current tools. • CAD tools need intuitive means for testing during modeling. • Inverse kinematic solvers allow for rapid prototyping and testing in CAD systems. • New user interface and interaction paradigms ease interaction with CAD tools.

This report demonstrates the use of a genetic algorithm search in the solution of an inverse problem. The genetic algorithm is used to solve the one-dimensional inverse heat conduction problem using numerical data generated by solution of the corresponding direct problem. Both "pure" and noisy data are considered. If used with regularization, the method is shown to yield reasonable results even in the face of noisy data. Although only a simple one-dimensional case is presented to demonstrate the method, the genetic algorithm approach is expected to excel in the solution of complicated multidimensional inverse problems.

The objective is a neural-based feature selection in intelligent recommender systems. In particular, a hybrid neural genetic architecture is modeled based on human nature, interactions, and behaviour. The main contribution of this chapter is the development of a novel genetic algorithm based on human nature, interactions, and behaviour. The novel genetic algorithm termed “Buabin Algorithm” is fully integrated with a hybrid neural classifier to form a Hybrid Neural Genetic Architecture. The research presents GA in a more attractive manner and opens up the various departments of a GA for active research. Although no scientific experiment is conducted to compare network performance with standard approaches, engaged techniques reveal drastic reductions in genetic operator operations. For illustration purposes, the UCI Molecular Biology (Splice Junction) dataset is used. Overall, “Buabin Algorithm” seeks to integrate human related interactions into genetic algorithms as imitate human genetics in recommender systems design and understand underlying datasets explicitly.

Humanoid robot path planning using memory-based gravity search algorithm and enhanced differential evolution approach in a complex environment