Redundant robotic systems, in terms of manipulators with one or twoarms, mobile manipulators, and multi-agent systems, have received an in-creasing amount of attention in recent years. In this thesis we describe severalways to improve robotic system performance by exploiting the redundancy. As the robot workspace becomes increasingly dynamic, it is common towork with imperfect geometric models of the robots or its workspace. Inorder to control the robot in a robust way in the presence of geometric uncer-tainties, we propose to assess the stability of our controller with respect to acertain task by deriving bounds on the geometric uncertainties. Preliminaryexperimental results support the fact that stability is ensured if the proposedbounds on the geometric uncertainties are fulfilled. As a non-contact measurement, computer vision could provide rich infor-mation for robot control. We introduce a two step method that transformsthe position-based visual servoing problem into a quadratic optimization prob-lem with linear constraints. This method is optimal in terms of minimizinggeodesic distance and allows us to integrate constraints, e.g. visibility con-straints, in a natural way. In the case of a single robot with redundant degrees of freedom, we canspecify a family of complex robotic tasks using constraint based programming(CBP). CBP allows us to represent robotic tasks with a set of equality andinequality constraints. Using these constraints we can formulate quadraticprogramming problems that exploit the redundancy of the robot and itera-tively resolve the trade-off between the different constraints. For example, wecould improve the velocity or force transmission ratios along a task-dependent direction using the priorities between different constraints in real time. Using the reactiveness of CBP, we formulated and implemented a dual-armpan cleaning task. If we mount a dual-arm robot on a mobile base, we proposeto use a virtual kinematic chain to specify the coordination between the mobilebase and two arms. Using the modularity of the CBP, we can integrate themobility and dual-arm manipulation by adding coordination constraints intoan optimization problem where dual-arm manipulation constraints are alreadyspecified. We also found that the reactiveness and modularity of the CBPapproach is important in the context of teleoperation. Inspired by the 3Ddesign community, we proposed a teleoperation interface control mode thatis identical to the ones being used to locally navigate the virtual viewpoint ofmost Computer Aided Design (CAD) softwares. In the case of multiple robots, we combine ideas from multi-agent coopera-tive coverage control, with problem formulations from the resource allocationfield, to create a distributed convergent approach to the resource positioningproblem. / <p>QC 20160224</p>

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This 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. / Doctor of Philosophy / Using mobile robots for autonomous victim extraction in disaster scenarios reduces the potential risk to field medical providers. This dissertation focuses on applying artificial intelligence techniques to this human-robot interaction task involving pose estimation and dual-arm manipulation for victim extraction. This work is based on a 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 attached on an embedded declining stretcher to achieve autonomous safe and effective victim extraction. Therefore, the overall research in this dissertation addresses: human pose estimation, robotic manipulator modeling, and dual-arm robotic manipulation for human pose adjustment. To accurately estimate the human pose for real-time applications, the dissertation proposes a neural network that could take advantages of multiple Graphics Processing Units (GPU). Considering the cost in data collection, the dissertation proposed novel machine learning techniques to obtain the inverse dynamic model and the inverse kinematic model of the robotic manipulators using limited collected data. Applying safety constraints is another requirement when robots interacts with humans. This dissertation proposes reinforcement learning techniques to efficiently train a dual-arm manipulation system not only to perform the basic behaviors, such as reaching, pushing objects and picking up and placing objects, but also to take safety constraints into consideration in performing tasks. Finally, the three components mentioned above are integrated together as a complete system. Experimental validation and results are discussed at the end of this dissertation.