Organizational Unit:
Humanoid Robotics Laboratory

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https://hdl.handle.net/1853/70718
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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.