Ren, Hailin2021-10-092021-10-092020-04-16vt_gsexam:24795http://hdl.handle.net/10919/105214This dissertation focuses on applying artificial intelligence techniques to human-robot interaction, which involves human pose estimation and dual-arm robotic manipulation. The motivating application behind this work is autonomous victim extraction in disaster scenarios using a conceptual design of a Semi-Autonomous Victim Extraction Robot (SAVER). SAVER is equipped with an advanced sensing system and two powerful robotic manipulators as well as a head and neck stabilization system to achieve autonomous safe and effective victim extraction, thereby reducing the potential risk to field medical providers. This dissertation formulates the autonomous victim extraction process using a dual-arm robotic manipulation system for human-robot interaction. According to the general process of Human-Robot Interaction (HRI), which includes perception, control, and decision-making, this research applies machine learning techniques to human pose estimation, robotic manipulator modeling, and dual-arm robotic manipulation, respectively. In the human pose estimation, an efficient parallel ensemble-based neural network is developed to provide real-time human pose estimation on 2D RGB images. A 13-limb, 14-joint skeleton model is used in this perception neural network and each ensemble of the neural network is designed for a specific limb detection. The parallel structure poses two main benefits: (1) parallel ensembles architecture and multiple Graphics Processing Units (GPU) make distributed computation possible, and (2) each individual ensemble can be deployed independently, making the processing more efficient when the detection of only some specific limbs is needed for the tasks. Precise robotic manipulator modeling benefits from the simplicity of the controller design and improves the performance of trajectory following. Traditional system modeling relies on first principles, simplifying assumptions and prior knowledge. Any imperfection in the above could lead to an analytical model that is different from the real system. Machine learning techniques have been applied in this field to pursue faster computation and more accurate estimation. However, a large dataset is always needed for these techniques, while obtaining the data from the real system could be costly in terms of both time and maintenance. In this research, a series of different Generative Adversarial Networks (GANs) are proposed to efficiently identify inverse kinematics and inverse dynamics of the robotic manipulators. One four-Degree-of-Freedom (DOF) robotic manipulator and one six-DOF robotic manipulator are used with different sizes of the dataset to evaluate the performance of the proposed GANs. The general methods can also be adapted to other systems, whose dataset is limited using general machine learning techniques. In dual-arm robotic manipulation, basic behaviors such as reaching, pushing objects, and picking objects up are learned using Reinforcement Learning. A Teacher-Student advising framework is proposed to learn a single neural network to control dual-arm robotic manipulators with previous knowledge of controlling a single robotic manipulator. Simulation and experimental results present the efficiency of the proposed framework compared to the learning process from scratch. Another concern in robotic manipulation is safety constraints. A variable-reward hierarchical reinforcement learning framework is proposed to solve sparse reward and tasks with constraints. A task of picking up and placing two objects to target positions while keeping them in a fixed distance within a threshold is used to evaluate the performance of the proposed method. Comparisons to other state-of-the-art methods are also presented. Finally, all the three proposed components are integrated as a single system. Experimental evaluation with a full-size manikin was performed to validate the concept of applying artificial intelligence techniques to autonomous victim extraction using a dual-arm robotic manipulation system.ETDIn CopyrightReinforcement LearningDeep learning (Machine learning)Human-Robot InteractionHuman Pose EstimationHuman Pose ManipulationDual-arm ManipulationDissertation