Humanoid Robotics Laboratory
Humanoid Robotics Laboratory
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ItemAch: IPC for Real-Time Robot Control(Georgia Institute of Technology, 2011) Dantam, Neil ; Stilman, Mike ; Georgia Institute of Technology. Center for Robotics and Intelligent MachinesWe 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.
ItemEquations of Motion for Dynamically Stable Mobile Manipulators(Georgia Institute of Technology, 2010-12-14) Dantam, Neil ; Kolhe, Pushkar ; Stilman, Mike ; Georgia Institute of Technology. Center for Robotics and Intelligent Machines ; Georgia Institute of Technology. School of Interactive Computing
ItemAlgorithms for Linguistic Robot Policy Inference from Demonstration of Assembly Tasks(Georgia Institute of Technology, 2012) Dantam, Neil ; Essa, Irfan ; Stilman, Mike ; Georgia Institute of Technology. Center for Robotics and Intelligent MachinesWe 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.
ItemThe Motion Grammar: Linguistic Perception, Planning, and Control(Georgia Institute of Technology, 2010) Dantam, Neil ; Stilman, Mike ; Georgia Institute of Technology. Center for Robotics and Intelligent MachinesWe present the Motion Grammar: a novel unified representation for task decomposition, perception, planning, and hybrid control that provides a computationally tractable way to control robots in uncertain environments with guarantees on 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 game-play for matches up to six minutes.