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Humanoid Robotics Laboratory

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Now showing 1 - 10 of 64
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    Robust and Efficient Communication for Real-Time Multi-Process Robot Software
    (Georgia Institute of Technology, 2012-11) Dantam, Neil ; Stilman, Mike ; Humanoid Robotics Laboratory ; Georgia Institute of Technology. College of Computing
    We present a new Interprocess Communication (IPC) mechanism and library. Ach is uniquely suited for coordinating drivers, controllers, and algorithms in complex robotic systems such as humanoid robots. 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, and we discuss the implementation on our humanoid robot Golem Krang. Finally, the source code for Ach is available under an Open Source permissive license.
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    Turning Paths Into Trajectories Using Parabolic Blends
    (Georgia Institute of Technology, 2011) Kunz, Tobias ; Stilman, Mike ; Humanoid Robotics Laboratory ; College of Computing
    We present an approach for converting a path of multiple continuous linear segments into a trajectory that satisfies velocity and acceleration constraints and closely follows the given path without coming to a complete stop at every waypoint. Our method applies parabolic blends around waypoints to improve speed. In contrast to established methods that smooth trajectories with parabolic blends, our method does not require the timing of waypoints or durations of blend phases. This makes our approach particularly useful for robots that must follow kinematic paths that are not explicitly parametrized by time. Our method chooses timing automatically to achieve high performance while satisfying the velocity and acceleration constraints of a given robot.
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    Planning Among Movable Obstacles with Artificial Constraints
    (Georgia Institute of Technology, 2008-11) Stilman, Mike ; Kuffner, James J. ; Humanoid Robotics Laboratory ; Carnegie-Mellon University. Robotics Institute
    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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    Algorithms for Linguistic Robot Policy Inference from Demonstration of Assembly Tasks
    (Georgia Institute of Technology, 2012) Dantam, Neil ; Essa, Irfan ; Stilman, Mike ; Humanoid Robotics Laboratory ; College of Computing
    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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    Towards Stable Balancing
    (Georgia Institute of Technology, 2014) Zafar, Munzir ; Erdogan, Can ; Stilman, Mike ; Humanoid Robotics Laboratory ; College of Computing
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    Probabilistic Human Action Prediction and Wait-sensitive Planning for Responsive Human-robot Collaboration
    (Georgia Institute of Technology, 2013-10) Hawkins, Kelsey P. ; Vo, Nam ; Bansal, Shray ; Bobic, Aaron F. ; Humanoid Robotics Laboratory ; Georgia Institute of Technology. College of Computing ; Georgia Institute of Technology. School of Interactive Computing
    A novel representation for the human component of multi-step, human-robot collaborative activity is presented. The goal of the system is to predict in a probabilistic manner when the human will perform different subtasks that may require robot assistance. The representation is a graphical model where the start and end of each subtask is explicitly represented as a probabilistic variable conditioned upon prior intervals. This formulation allows the inclusion of uncertain perceptual detections as evidence to drive the predictions. Next, given a cost function that describes the penalty for different wait times, we develop a planning algorithm which selects robot-actions that minimize the expected cost based upon the distribution over predicted human-action timings. We demonstrate the approach in assembly tasks where the robot must provide the right part at the right time depending upon the choices made by the human operator during the assembly.
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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. ; Humanoid Robotics Laboratory ; National Institute of Advanced Industrial Science and Technology (Japan). Digital Human Research Center
    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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    Push Planning for Object Placement in Clutter Using the PR-2
    (Georgia Institute of Technology, 2011-09) Emeli, Victor ; Kemp, Charles C. ; Stilman, Mike ; Humanoid Robotics Laboratory
    The goal of this project is to investigate the implementation of a planning algorithm for the problem of placing objects on a cluttered surface with a PR-2 mobile manipulator. The original push planning algorithm [1] was initially developed as a simulation. We modified the simulator for execution in real-world cluttered environments. This paper discusses the challenges of implementation and presents empirical results that determine how well the simulator models the real world as clutter is pushed and collides with other objects.
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    The Motion Grammar: Linguistic Perception, Planning, and Control
    (Georgia Institute of Technology, 2011-06) Dantam, Neil ; Stilman, Mike ; Humanoid Robotics Laboratory ; Georgia Institute of Technology. School of Interactive Computing
    We present and analyze the Motion Grammar: a novel unified representation for task decomposition, perception, planning, and control that provides both fast online control of robots in uncertain environments and the ability to guarantee completeness and correctness. The grammar represents a policy for the task which is parsed in real-time based on perceptual input. Branches of the syntax tree form the levels of a hierarchical decomposition, and the individual robot sensor readings are given by tokens. We implement this approach in the interactive game of Yamakuzushi on a physical robot resulting in a system that repeatably competes with a human opponent in sustained gameplay for the roughly six minute duration of each match.
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    Dynamic Pushing Strategies for Dynamically Stable Mobile Manipulators
    (Georgia Institute of Technology, 2010-05) Kolhe, Pushkar ; Dantam, Neil ; Stilman, Mike ; Humanoid Robotics Laboratory ; Georgia Institute of Technology. School of Interactive Computing
    This paper presents three effective manipulation strategies for wheeled, dynamically balancing robots with articulated links. By comparing these strategies through analysis, simulation and robot experiments, we show that contact placement and body posture have a significant impact on the robot's ability to accelerate and displace environment objects. Given object geometry and friction parameters we determine the most effective methods for utilizing wheel torque to perform non-prehensile manipulation.