Organizational Unit:
Humanoid Robotics Laboratory

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Publication Search Results

Now showing 1 - 10 of 16
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    Krang Kinematics: A Denavit-Hartenberg Parameterization
    (Georgia Institute of Technology, 2014) Erdogan, Can ; Zafar, Munzir ; Stilman, Mike
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    Krang: Center of Mass Estimation
    (Georgia Institute of Technology, 2014) Zafar, Munzir ; Erdogan, Can ; Volle, Kyle ; Stilman, Mike
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    Towards Stable Balancing
    (Georgia Institute of Technology, 2014) Zafar, Munzir ; Erdogan, Can ; Stilman, Mike
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    Gravity and Drift in Force/Torque Measurements
    ( 2014) Erdogan, Can ; Zafar, Munzir ; Stilman, Mike
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    Kinematics and Inverse Kinematics for the Humanoid Robot HUBO2+
    (Georgia Institute of Technology, 2013) O’Flaherty, Rowland ; Vieira, Peter ; Grey, Michael ; Oh, Paul ; Bobick, Aaron F. ; Egerstedt, Magnus B. ; Stilman, Mike
    This paper derives the forward and inverse kinematics of a humanoid robot. The specific humanoid that the derivation is for is a robot with 27 degrees of freedom but the procedure can be easily applied to other similar humanoid platforms. First, the forward and inverse kinematics are derived for the arms and legs. Then, the kinematics for the torso and the head are solved. Finally, the forward and inverse kinematic solutions for the whole body are derived using the kinematics of arms, legs, torso, and head.
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    Linguistic Transfer of Human Assembly Tasks to Robots
    (Georgia Institute of Technology, 2012-10) Dantam, Neil ; Essa, Irfan ; Stilman, Mike
    We demonstrate the automatic transfer of an assembly task from human to robot. This work extends efforts showing the utility of linguistic models in verifiable robot control policies by now performing real visual analysis of human demonstrations to automatically extract a policy for the task. This method tokenizes each human demonstration into a sequence of object connection symbols, then transforms the set of sequences from all demonstrations into an automaton, which represents the task-language for assembling a desired object. Finally, we combine this assembly automaton with a kinematic model of a robot arm to reproduce the demonstrated task.
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    Diverse Workspace Path Planning for Robot Manipulators
    (Georgia Institute of Technology, 2012-07) Quispe, Ana Huamán ; Stilman, Mike
    We present a novel algorithm that generates a set of diverse workspace paths for manipulators. By considering more than one possible path we give our manipulator the flexibility to choose from many possible ways to execute a task. This is particularly important in cases in which the best workspace path cannot be executed by the manipulator (e.g. due to the presence of obstacles that collide with the manipulator links). Our workspace paths are generated such that a distance metric between them is maximized, allowing them to span different workspace regions. Manipulator planners mostly focus on solving the problem by analyzing the configuration space (e.g. Jacobian-based methods); our approach focuses on analyzing alternative workspace paths which are comparable to the optimal solution in terms of length. This paper introduces our intuitive algorithm and also presents the results of a series of experiments performed with a simulated 7 DOF robotic arm.
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    Detecting Partially Occluded Objects via Segmentation and Validation
    (Georgia Institute of Technology, 2012) Levihn, Martin ; Dutton, Matthew ; Trevor, Alexander J. B. ; Stilman, Mike
    This paper presents a novel algorithm: Verfied Partial Object Detector (VPOD) for accurate detection of partially occluded objects such as furniture in 3D point clouds. VPOD is implemented and validated on real sensor data obtained by our robot. It extends Viewpoint Feature Histograms (VFH) which classify unoccluded objects to also classifying partially occluded objects such as furniture that might be seen in typical office environments. To achieve this result, VPOD employs two strategies. First, object models are segmented and the object database is extended to include partial models. Second, once a matching partial object is detected, the full object model is aligned back into the scene and verified for consistency with the point cloud data. Overall, our approach increases the number of objects found and substantially reduces false positives due to the verification process.
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    Algorithms for Linguistic Robot Policy Inference from Demonstration of Assembly Tasks
    (Georgia Institute of Technology, 2012) Dantam, Neil ; Essa, Irfan ; Stilman, Mike
    We describe several algorithms used for the inference of linguistic robot policies from human demonstration. First, tracking and match objects using the Hungarian Algorithm. Then, we convert Regular Expressions to Nondeterministic Finite Automata (NFA) using the McNaughton-Yamada-Thompson Algorithm. Next, we use Subset Construction to convert to a Deterministic Finite Automaton. Finally, we minimize finite automata using either Hopcroft's Algorithm or Brzozowski's Algorithm.
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    Ach: IPC for Real-Time Robot Control
    (Georgia Institute of Technology, 2011) Dantam, Neil ; Stilman, Mike
    We present a new Inter-Process Communication (IPC) mechanism and library. Ach is uniquely suited for coordinating perception, control drivers, and algorithms in real-time systems that sample data from physical processes. Ach eliminates the Head-of-Line Blocking problem for applications that always require access to the newest message. Ach is efficient, robust, and formally verified. It has been tested and demonstrated on a variety of physical robotic systems. Finally, the source code for Ach is available under an Open Source BSD-style license.