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Institute for Robotics and Intelligent Machines (IRIM)
Institute for Robotics and Intelligent Machines (IRIM)
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ItemGrasp selection strategies for robot manipulation using a superquadric-based object representation(Georgia Institute of Technology, 2016-07-29) Huaman, Ana ConsueloThis thesis presents work on the implementation of a robotic system targeted to perform a set of basic manipulation tasks instructed by a human user. The core motivation on the development of this system was in enabling our robot to achieve these tasks reliably, in a time-efficient manner and under mildly realistic constraints. Robot manipulation as a field has grown exponentially during the last years, presenting us with a vast array of robots exhibiting skills as sophisticated as preparing dinner, making an expresso or operating a drill. These complex tasks are in general achieved by using equally complex frameworks, assuming extensive pre-existing knowledge, such as perfect environment knowledge, sizable amounts of training data or availability of crowdsourcing resources. In this work we postulate that elementary tasks, such as pick-up, pick-and-place and pouring, can be realized with online algorithms and a limited knowledge of the objects to be manipulated. The presented work shows a fully implemented pipeline where each module is designed to meet the core requirements specified above. We present a number of experiments involving a database of 10 household objects used in 3 selected elementary manipulation tasks. Our contributions are distributed in each module of our pipeline: (1) We demonstrate that superquadrics are useful primitive shapes suitable to represent on-the-fly a considerable number of convex household objects; their parametric nature (3 axis and 2 shape parameters) is shown to be helpful to represent simple semantic labels for objects (i.e. for a pouring task) useful for grasp and motion planning. (2) We introduce a hand-and-arm metric that considers both grasp robustness and arm end-comfort to select grasps for simple pick-up tasks. We show with real and simulation results that considering both hand and arm aspects of the manipulation task helps to select grasps that are easier to execute in real environments without sacrificing grasp stability on the process. (3) We present grasp selection and planning strategies that exploit task constraints to select the more appropriate grasp to carry out a manipulation task in an online and efficient manner (in terms of planning and execution time).
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ItemPlanning in constraint space for multi-body manipulation tasks(Georgia Institute of Technology, 2016-04-05) Erdogan, CanRobots are inherently limited by physical constraints on their link lengths, motor torques, battery power and structural rigidity. To thrive in circumstances that push these limits, such as in search and rescue scenarios, intelligent agents can use the available objects in their environment as tools. Reasoning about arbitrary objects and how they can be placed together to create useful structures such as ramps, bridges or simple machines is critical to push beyond one's physical limitations. Unfortunately, the solution space is combinatorial in the number of available objects and the configuration space of the chosen objects and the robot that uses the structure is high dimensional. To address these challenges, we propose using constraint satisfaction as a means to test the feasibility of candidate structures and adopt search algorithms in the classical planning literature to find sufficient designs. The key idea is that the interactions between the components of a structure can be encoded as equality and inequality constraints on the configuration spaces of the respective objects. Furthermore, constraints that are induced by a broadly defined action, such as placing an object on another, can be grouped together using logical representations such as Planning Domain Definition Language (PDDL). Then, a classical planning search algorithm can reason about which set of constraints to impose on the available objects, iteratively creating a structure that satisfies the task goals and the robot constraints. To demonstrate the effectiveness of this framework, we present both simulation and real robot results with static structures such as ramps, bridges and stairs, and quasi-static structures such as lever-fulcrum simple machines.
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ItemTrust and reputation for formation and evolution of multi-robot teams(Georgia Institute of Technology, 2013-11-15) Pippin, Charles EverettAgents in most types of societies use information about potential partners to determine whether to form mutually beneficial partnerships. We can say that when this information is used to decide to form a partnership that one agent trusts another, and when agents work together for mutual benefit in a partnership, we refer to this as a form of cooperation. Current multi-robot teams typically have the team's goals either explicitly or implicitly encoded into each robot's utility function and are expected to cooperate and perform as designed. However, there are many situations in which robots may not be interested in full cooperation, or may not be capable of performing as expected. In addition, the control strategy for robots may be fixed with no mechanism for modifying the team structure if teammate performance deteriorates. This dissertation investigates the application of trust to multi-robot teams. This research also addresses the problem of how cooperation can be enabled through the use of incentive mechanisms. We posit a framework wherein robot teams may be formed dynamically, using models of trust. These models are used to improve performance on the team, through evolution of the team dynamics. In this context, robots learn online which of their peers are capable and trustworthy to dynamically adjust their teaming strategy. We apply this framework to multi-robot task allocation and patrolling domains and show that performance is improved when this approach is used on teams that may have poorly performing or untrustworthy members. The contributions of this dissertation include algorithms for applying performance characteristics of individual robots to task allocation, methods for monitoring performance of robot team members, and a framework for modeling trust of robot team members. This work also includes experimental results gathered using simulations and on a team of indoor mobile robots to show that the use of a trust model can improve performance on multi-robot teams in the patrolling task.