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Humanoid Robotics Laboratory

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Diverse Workspace Path Planning for Robot Manipulators

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

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

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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