Organizational Unit:
Humanoid Robotics Laboratory

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Now showing 1 - 5 of 5
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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
    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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    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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    The Motion Grammar Calculus for Context-Free Hybrid Systems
    (Georgia Institute of Technology, 2012-06) Dantam, Neil ; Stilman, Mike
    This paper provides a method for deriving provably correct controllers for Hybrid Dynamical Systems with Context-Free discrete dynamics, nonlinear continuous dynamics, and nonlinear state partitioning. The proposed method models the system using a Context-Free Motion Grammar and specifies correct performance using a Regular language representation such as Linear Temporal Logic. The initial model is progressively rewritten via a calculus of symbolic transformation rules until it satisfies the desired specification.
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    Linguistic Composition of Semantic Maps and Hybrid Controllers
    (Georgia Institute of Technology, 2012-06) Dantam, Neil ; Nieto-Granda, Carlos ; Christensen, Henrik I. ; Stilman, Mike
    This work combines semantic maps with hybrid control models, generating a direct link between action and environment models to produce a control policy for mobile manipulation in unstructured environments. First, we generate a semantic map for our environment and design a base model of robot action. Then, we combine this map and action model using the Motion Grammar Calculus to produce a combined robot-environment model. Using this combined model, we apply supervisory control to produce a policy for the manipulation task. We demonstrate this approach on a Segway RMP-200 mobile platform.
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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.