774 research outputs found
Arbitrary surface modeling in the common reality framework for human-robot collaboration
No full textResearch on HONGSHENG
No full text洪昇是中国古代文学史、中国古代戏曲史上非常伟大的传奇作家和戏曲家。他出生于“累叶清华”的仕宦之家，“少负英绝之才”，早擅文名，但终生是一个布衣才子。洪昇命运多舛，一生坎坷。 在多灾多难的一生中，洪昇留下了颇为丰富的文学作品。洪昇才华横溢，博涉多能，其诗文词曲均有传世之作。洪昇一生创作了千余首诗歌，在其诗歌结集时，因以流传久远为念而痛加删削，仅有五百余首流传下来。其诗“高超闲淡，不落凡境”，自成一家，受到当世硕学王士禛、朱彝尊等人推崇。《诗骚韵注》是其早年“性近韵学”的成果，其书对韵学“穷其原委”，虽未能在韵学理论上有所突破，但颇具有学术研究性质。洪昇尤其钟情于词曲的创作，但其词作并未有文集流...Hongsheng , an outstanding ancient author ，was born into an official family. He had his fame when he was very young because of his gifted in literary writing, but life was never bright for him and he suffered a lot. Hongsheng was extremely talent and versatile and his ci pomes and qu verses have lasted until present day. He was especially excellent at composing ci poms and qu verses, but pitif...学位：博士后院系专业：人文学院历史学系_中国古代史学号：BH1700012
Koopman Theory for Boolean Networks
No full textNetworks of agents with logical states, namely Boolean networks, arise in various application domains including biology, computer networks, and social networks. The representation and control of Boolean networks have attracted much attention in recent years. In a parallel line of research, Koopman developed an operator view of nonlinear dynamical systems, which shows that, by making use of observable functions, every nonlinear dynamics can be represented as a (possibly infinite dimensional) linear system. In this paper, we present a Koopman theory for Boolean networks. We introduce algebraic operations for logical functions over semirings of binary numbers, defining a semimodule of Boolean functions. The classical Koopman operator and Koopman theory are then established for Boolean networks, where the key ingredient to a linear representation is shown to be Koopman invariance. Next, we establish a necessary and sufficient condition for shaping the closed-loop dynamics via feedback into any desired form for Boolean control networks under the Koopman representation, and we develop a feedback control synthesis algorithm to solve this feedback shaping problem. Finally, feedback shaping is applied to a real-world gene regulatory network, and it is demonstrated that the dynamics of such a network may be arbitrarily manipulated by means of feedback control
Understanding abstract sketches by reinforcement learning for automated robotic assembly
No full textThesis (M.S.)-- Wichita State University, College of Engineering, Dept. of Electrical Engineering and Computer ScienceThis paper presents a method to understand and reason the spatial configuration of individual parts in an assembled model from a 2D ISO sketch for autonomous small-part assembly. When given disassembled individual parts for a model in a 3D environment and an isometric 2D sketch of the assembled model, a human can tell visually the spatial configuration (position and orientation vectors) of the parts according to the assembly sketch, but it is challenging for a robot to decipher it. To understand the isometric drawings, a new reinforcement learning architecture is designed, which takes in an isometric drawing sketch as the reference and learns to reconfigure the spatial configuration of the parts to minimize the difference from the reference. A learning environment framework was created based on FreeCAD for reinforcement learning (RL) agent to interact with 3D models. The difference between the reference and current isometric sketches is calculated in the reward function, which guides the RL agent to move the 3D models to the target configurations. The proximal policy optimization (PPO) function was used for the RL agent because of its reliability and high training speed. To further augment generalization and robustness of the PPO model, an LSTM model was introduced into the RL architecture with a continuous action space. The proposed architecture demonstrated convincing performance in the experiment in FreeCAD. The performance indices include convergence on standard industrial assembly images within an average of about 60 steps, generalization on different initial configurations, and robustness to subtle nuances in a given reference sketch.
Semantic scene understanding for intelligent robotics
No full textThesis (Ph.D.)-- Wichita State University, College of Engineering, School of ComputingThis dissertation focuses on improving robotic scene semantics understanding and
developing a new human-robot interaction (HRI) interface based on augmented reality
(AR). To achieve a deep scene understanding, the proposed scene semantics understanding
method consists of three parts: object detection, object semantic comprehension, and
feedback on robotic comprehension. The method analyzes detected objects’ category,
function, property, and composition to enable robots to understand object semantics and
reason relations between objects. Additionally, the dissertation proposes a method for an
intelligent industrial robot to comprehend spatial constraints for model assembly. The
proposed method uses an extended generative adversary network (GAN) with a 3D long
short-term memory (LSTM) network to composite 3D point clouds from a single or a few
multiple-depth scans. The spatial constraints of the segmented point clouds are identified
by a neural-logic network that incorporates general knowledge of spatial constraints in
terms of first-order logic. The proposed HRI interface superimposes robot-centered and
human-centered reality on the working space to construct a mutual understanding
environment. The interface enables humans to communicate with robots through speech
and immersive touching, constructing mutual understanding through the user’s commands,
localization and recognition of objects, object semantics, and augmented trajectory. The
user’s vocal commands are interpreted to formal logic, and finger touching is detected and
coordinated. Real-world experiments show the effectiveness of the proposed interface
Symbolic reasoning and learning of spatial relationships for robotic comprehension
No full textPresented to the 15th Annual Symposium on Graduate Research and Scholarly Projects (GRASP) held at the Rhatigan Student Center, Wichita State University, April 26, 2019.Research completed in the Department of Electrical Engineering and Computer Science, College of EngineeringGiven the visual information of objects in a scene, humans can reason spatial relationships of those objects. To achieve a similar goal with robots is considerably a challenging task even though robotic technology is developing significantly. Comprehension of spatial relationships of objects in the workspace plays an important role in Human-Robot Interaction (HRI). This paper proposes a method which enables robots to comprehend spatial relationships among objects in shared environments by using visual information. A neural symbolic learning framework is introduced for that purpose, which integrates advantages of both numerical learning and logic inference. Spatial relationships which are held between objects that are represented in form of logic rules such as ∀,: (, ) → ¬(, ).
Logic rules which describe spatial relationships are mapped to a numerical data-space in form of feature vectors. RGB-D data is used to feed into numerical model to learn spatial rules for robot reasoning. The embedded RGB-D sensor collects aligned depth images for numerical learning. In the Figure below, RGB-D data of objects in the scene has been captured by the Zivid sensor, recognized objects are assigned with labels, then a trained neural symbolic framework is used to reason spatial relationships. The spatial relationships comprehension by robots is evaluated by both simulations and execution of the Sawyer robot and the AR 10 robot hand.Graduate School, Academic Affairs, University Librarie
Understanding spatial constraints for autonomous robotic assembly with neural logic learning
No full textPresented to the 17th Annual Symposium on Graduate Research and Scholarly Projects (GRASP) held online, Wichita State University, April 2, 2021.Research completed in the Department of Electrical Engineering and Computer Science, College of EngineeringSpatial constraints of objects are one of the key elements that are required in industrial assembly. Robots deployed in conventional assembly lines are based on schema by referring to computer-aided design (CAD) software. Spatial constraints are modeled by computer-aided design (CAD) software. Compared with conventional assembly lines, autonomous robotic assembly requires robots to learn spatial constraints intelligently. Therefore, understanding spatial constraints are critical for autonomous robotic assembly. This work proposed a method to address the critical need of enabling robots to comprehend spatial constraints with a single RGB-D scan. The proposed method contains two parts: the first one generates 3D models to fulfill the missing point-cloud of a single RGB-D scan of objects with an extended generative adversary network (GAN). The second part enables robots to comprehended spatial constraints with a neural-logic network. The spatial constraints include left, right, above, below, front, behind, parallel, perpendicular, concentric, and coincident. The 3D composition model achieved 57.23% intersection over union (IoU), and the neural logic model that can learn spatial constraints achieved over 99% in comprehending all spatial constraints.Graduate School, Academic Affairs, University Librarie
Probabilistic learning of robotic grasping strategy based on natural language object descriptions
No full textThesis (M.S.)-- Wichita State University, College of Engineering, Dept. of Industrial Systems, and Manufacturing EngineeringHumans learn to be dexterous by interacting with a wide variety of objects in
different contexts. Given the description of an object’s physical attributes, humans can
determine a proper strategy and grasp an object. This paper proposes an approach to
determine grasping strategy for a 10 degree-of-freedom anthropomorphic robotic hand
simply based on natural-language descriptions of an object. A probabilistic learning-based
approach is proposed to help a robotic hand learn suitable grasp poses starting from the
natural language description of the object. The solution involves a three-step learning
model. In the first step, the information parsed from an object’s natural-language
descriptions are used to identify/recognize the object by making use of a novel nearestneighbor distance metric. In the second step, the probability distribution of grasp types
for the given object is learned using a deep neural net which takes in object features as
input. The labels for this grasp learning model is supplied from human grasping trials. The
discrete, two-dimensional grasp type/size vector is mapped back to the ten-dimensional
robot joint-angles configuration space using linear inverse-kinematics models. The
grasping strategy generated by the proposed approach is evaluated both by simulation
study and execution of the grasps on an AR10 robotic hand.
Index Terms—robotic grasping, human grasp primitives, natural language
processing, object features extraction, neural networks classification
Omnisurface: Common reality for intuitive human-robot collaboration
No full textChapter 34 of Social RoboticsEffective communication and information projection are essential for human-robot teaming. The projection of images on nonplanar surfaces using a conventional projector is challenging due to the inherent problem of distortion. The projection distortion occurs due to the variations in depth across the surface of the teaming workspace. As a result, the projected image, information, or symbols lose their original shape and create confusion during human-robot teaming. In this paper, we presented an innovative approach to perform distortion-free projections in the teaming workspace. A pre-warped image is constructed based on the surface geometry that the projector displays and accurately replicates the original projection image. Beyond the technical achievement, this research highlights the social acceptance of improved spatial augmented reality in human-robot teams. It fosters better teamwork, trust, and efficiency by enabling more intuitive and reliable interactions
Learning robotic grasping strategy based on natural-language object descriptions
No full text1st place award winner in the poster presentations at the 14th Annual Symposium on Graduate Research and Scholarly Projects (GRASP) held at the Rhatigan Student Center, Wichita State University, April 27, 2018.Research completed in the Department of Industrial, Systems and Manufacturing, College of Engineering and Department of Electrical Engineering and Computer Science, College of EngineeringGiven the description of an object's physical attributes, humans can determine a proper strategy and grasp an object. Accomplishing a similar feat with robotic hands is considerably challenging despite significant progress in robotics technology. Most objects of daily use are designed for human manipulation. It follows then that, humanoid robotic hands need to emulate human grasps and use a learning-based approach to learn to grasp and manipulate such objects.
This paper proposes an approach to determine grasping strategy for an anthropomorphic robotic hand simply based on natural-language descriptions of an object. An artificial neural network(ANN) based learning-based approach is proposed to help a robotic hand learn suitable grasp poses starting from the natural language description of the object. Object features such as shape, size, rigidity and mass are parsed from natural-language descriptions of everyday objects using a customized natural-language processing(NLP) technique. Based on the parsed features, the most likely human-like grasp type for the given object is learned from the human grasping taxonomy using a neural network classifier. The grasping strategy generated by the proposed artificial intelligence model is evaluated both by simulation study and execution of the grasps on an AR10 robotic hand.Graduate School, Academic Affairs, University Librarie
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