Organizational Unit:
Humanoid Robotics Laboratory

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Now showing 1 - 10 of 12
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    Optimized Control Strategies for Wheeled Humanoids and Mobile Manipulators
    (Georgia Institute of Technology, 2009-12) Stilman, Mike ; Wang, Jiuguang ; Teeyapan, Kasemsit ; Marceau, Ray
    Optimizing the control of articulated mobile robots leads to emergent behaviors that improve the effectiveness, efficiency and stability of wheeled humanoids and dynamically stable mobile manipulators. Our simulated results show that optimization over the target pose, height and control parameters results in effective strategies for standing, acceleration and deceleration. These strategies improve system performance by orders of magnitude over existing controllers. This paper presents a simple controller for robot motion and an optimization method for choosing its parameters. By using whole-body articulation, we achieve new skills such as standing and unprecedented levels of performance for acceleration and deceleration of the robot base. We describe a new control architecture, present a method for optimization, and illustrate its functionality through two distinct methods of simulation.
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    Robot Jenga: Autonomous and Strategic Block Extraction
    (Georgia Institute of Technology, 2009) Wang, Jiuguang ; Rogers, Philip ; Parker, Lonnie T. ; Brooks, Douglas Antwonne ; Stilman, Mike
    This paper describes our successful implementation of a robot that autonomously and strategically removes multiple blocks from an unstable Jenga tower. We present an integrated strategy for perception, planning and control that achieves repeatable performance in this challenging physical domain. In contrast to previous implementations, we rely only on low-cost, readily available system components and use strategic algorithms to resolve system uncertainty. We present a three-stage planner for block extraction which considers block selection, extraction order, and physics-based simulation that evaluates removability. Existing vision techniques are combined in a novel sequence for the identification and tracking of blocks within the tower. Discussion of our approach is presented following experimental results on a 5-DOF robot manipulator.
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    Planning Among Movable Obstacles with Artificial Constraints
    (Georgia Institute of Technology, 2008-11) Stilman, Mike ; Kuffner, James J.
    This paper presents artificial constraints as a method for guiding heuristic search in the computationally challenging domain of motion planning among movable obstacles. The robot is permitted to manipulate unspecified obstacles in order to create space for a path. A plan is an ordered sequence of paths for robot motion and object manipulation. We show that under monotone assumptions, anticipating future manipulation paths results in constraints on both the choice of objects and their placements at earlier stages in the plan. We present an algorithm that uses this observation to incrementally reduce the search space and quickly find solutions to previously unsolved classes of movable obstacle problems. Our planner is developed for arbitrary robot geometry and kinematics. It is presented with an implementation for the domain of navigation among movable obstacles.
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    Humanoid Teleoperation for Whole Body Manipulation
    (Georgia Institute of Technology, 2008-05) Stilman, Mike ; Nishiwaki, Koichi ; Kagami, Satoshi
    We present results of successful telemanipulation of large, heavy objects by a humanoid robot. Using a single joystick the operator controls walking and whole body manipulation along arbitrary paths for up to ten minutes of continuous execution. The robot grasps, walks, pushes, pulls, turns and re-grasps a 55kg range of loads on casters. Our telemanipulation framework changes reference frames online to let the operator steer the robot in free walking, its hands in grasping and the object during mobile manipulation. In the case of manipulation, our system computes a robot motion that satisfies the commanded object path as well as the kinematic and dynamic constraints of the robot. Furthermore, we achieve increased robot stability by learning dynamic friction models of manipulated objects.
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    Path Planning Among Movable Obstacles: A Probabilistically Complete Approach
    (Georgia Institute of Technology, 2008) van den Berg, Jur ; Stilman, Mike ; Kuffner, James ; Lin, Ming ; Manocha, Dinesh
    In this paper we study the problem of path planning among movable obstacles, in which a robot is allowed to move the obstacles if they block the robot's way from a start to a goal position. We make the observation that we can decouple the computations of the robot motions and the obstacle movements, and present a probabilistically complete algorithm, something which to date has not been achieved for this problem. Our algorithm maintains an explicit representation of the robot's configuration space. We present an efficient implementation for the case of planar, axis-aligned environments and report experimental results on challenging scenarios.
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    Learning Object Models for Humanoid Manipulation
    (Georgia Institute of Technology, 2007-11) Stilman, Mike ; Nishiwaki, Koichi ; Kagami, Satoshi
    We present a successful implementation of rigid grasp manipulation for large objects moved along specified trajectories by a humanoid robot. HRP-2 manipulates tables on casters with a range of loads up to its own mass. The robot maintains dynamic balance by controlling its center of gravity to compensate for reflected forces. To achieve high performance for large objects with unspecified dynamics the robot learns a friction model for each object and applies it to torso trajectory generation. We empirically compare this method to a purely reactive strategy and show a significant increase in predictive power and stability.
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    Task Constrained Motion Planning in Robot Joint Space
    (Georgia Institute of Technology, 2007-10) Stilman, Mike
    We explore global randomized joint space path planning for articulated robots that are subject to task space constraints. This paper describes a representation of constrained motion for joint space planners and develops two simple and efficient methods for constrained sampling of joint configurations: Tangent Space Sampling (TS) and First-Order Retraction (FR). Constrained joint space planning is important for many real world problems involving redundant manipulators. On the one hand, tasks are designated in work space coordinates: rotating doors about fixed axes, sliding drawers along fixed trajectories or holding objects level during transport. On the other, joint space planning gives alternative paths that use redundant degrees of freedom to avoid obstacles or satisfy additional goals while performing a task. In simulation, we demonstrate that our methods are faster and significantly more invariant to problem/algorithm parameters than existing techniques.
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    Manipulation Planning Among Movable Obstacles.
    (Georgia Institute of Technology, 2007-04) Stilman, Mike ; Schamburek, Jan-Ullrich ; Kuffner, James ; Asfour, Tamin
    This paper presents the ResolveSpatialConstraints (RSC) algorithm for manipulation planning in a domain with movable obstacles. Empirically we show that our algorithm quickly generates plans for simulated articulated robots in a highly nonlinear search space of exponential dimension. RSC is a reverse-time search that samples future robot actions and constrains the space of prior object displacements. To optimize the efficiency of RSC, we identify methods for sampling object surfaces and generating connecting paths between grasps and placements. In addition to experimental analysis of RSC, this paper looks into object placements and task-space motion constraints among other unique features of the three dimensional manipulation planning domain.
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    Planning and Executing Navigation Among Movable Obstacles
    (Georgia Institute of Technology, 2007) Stilman, Mike ; Nishiwaki, Koichi ; Kagami, Satoshi ; Kuffner, James J.
    This paper explores autonomous locomotion, reaching, grasping and manipulation for the domain of Navigation Among Movable Obstacles (NAMO). The robot perceives and constructs a model of an environment filled with various fixed and movable obstacles, and automatically plans a navigation strategy to reach a desired goal location. The planned strategy consists of a sequence of walking and compliant manipulation operations. It is executed by the robot with online feedback. We give an overview of our NAMO system, as well as provide details of the autonomous planning, online grasping and compliant hand positioning during dynamically-stable walking. Finally, we present results of a successful implementation running on the Humanoid Robot HRP-2.
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    Humanoid HRP2-DHRC for Autonomous and Interactive Behavior
    (Georgia Institute of Technology, 2007) Kagami, Satoshi ; Nishiwaki, K. ; Kuffner, James ; Thompson, S. ; Chestnutt, J. ; Stilman, Mike ; Michel, P.
    Recently, research on humanoid-type robots has become increasingly active, and a broad array of fundamental issues are under investigation. However, in order to achieve a humanoid robot which can operate in human environments, not only the fundamental components themselves, but also the successful integration of these components will be required. At present, almost all humanoid robots that have been developed have been designed for bipedal locomotion experiments. In order to satisfy the functional demands of locomotion as well as high-level behaviors, humanoid robots require good mechanical design, hardware, and software which can support the integration of tactile sensing, visual perception, and motor control. Autonomous behaviors are currently still very primitive for humanoid-type robots. It is difficult to conduct research on high-level autonomy and intelligence in humanoids due to the development and maintenance costs of the hardware. We believe low-level autonomous functions will be required in order to conduct research on higher-level autonomous behaviors for humanoids.