GPAtlasRRT: A local tactile exploration planner for recovering the shape of novel objects.

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GPAtlasRRT: A local tactile exploration planner for recovering the shape of novel objects. / Rosales, Carlos; Spinelli, Federico; Gabiccini, Maco ; Zito, Claudio; Wyatt, Jeremy.

In: International Journal of Humanoid Robotics, Vol. 15, 1850014, 01.02.2018.

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@article{a86d9133a1be4584a4f1d2da69984c69,
title = "GPAtlasRRT: A local tactile exploration planner for recovering the shape of novel objects.",
abstract = "Touch is an important modality to recover object shape. We present a method for a robot to complete a partial shape model by local tactile exploration. In local tactile exploration the finger is constrained to follow the local surface. This is useful for recovering information about a contiguous portion of the object and is frequently employed by humans. There are three contributions. First, we show how to segment an initial point cloud of a grasped, unknown object into hand and object. Second, we present a local tactile ex- ploration planner. This combines a Gaussian Process (GP) model of the object surface with an AtlasRRT planner. The GP predicts the unexplored surface and the uncertainty of that prediction. The AtlasRRT creates a tactile exploration path across this predicted surface, driving it towards the region of greatest uncertainty. Finally, we experimentally compare the planner with alternatives in simulation, and demonstrate the complete approach on a real robot. We show that our planner successfully traverses the object, and that the full object shape can be recovered with a good degree of accuracy.",
keywords = "Shape modeling, Tactile exploration",
author = "Carlos Rosales and Federico Spinelli and Maco Gabiccini and Claudio Zito and Jeremy Wyatt",
year = "2018",
month = feb
day = "1",
doi = "10.1142/S0219843618500147",
language = "English",
volume = "15",
journal = "International Journal of Humanoid Robotics",
issn = "0219-8436",
publisher = "World Scientific",

}

RIS

TY - JOUR

T1 - GPAtlasRRT: A local tactile exploration planner for recovering the shape of novel objects.

AU - Rosales, Carlos

AU - Spinelli, Federico

AU - Gabiccini, Maco

AU - Zito, Claudio

AU - Wyatt, Jeremy

PY - 2018/2/1

Y1 - 2018/2/1

N2 - Touch is an important modality to recover object shape. We present a method for a robot to complete a partial shape model by local tactile exploration. In local tactile exploration the finger is constrained to follow the local surface. This is useful for recovering information about a contiguous portion of the object and is frequently employed by humans. There are three contributions. First, we show how to segment an initial point cloud of a grasped, unknown object into hand and object. Second, we present a local tactile ex- ploration planner. This combines a Gaussian Process (GP) model of the object surface with an AtlasRRT planner. The GP predicts the unexplored surface and the uncertainty of that prediction. The AtlasRRT creates a tactile exploration path across this predicted surface, driving it towards the region of greatest uncertainty. Finally, we experimentally compare the planner with alternatives in simulation, and demonstrate the complete approach on a real robot. We show that our planner successfully traverses the object, and that the full object shape can be recovered with a good degree of accuracy.

AB - Touch is an important modality to recover object shape. We present a method for a robot to complete a partial shape model by local tactile exploration. In local tactile exploration the finger is constrained to follow the local surface. This is useful for recovering information about a contiguous portion of the object and is frequently employed by humans. There are three contributions. First, we show how to segment an initial point cloud of a grasped, unknown object into hand and object. Second, we present a local tactile ex- ploration planner. This combines a Gaussian Process (GP) model of the object surface with an AtlasRRT planner. The GP predicts the unexplored surface and the uncertainty of that prediction. The AtlasRRT creates a tactile exploration path across this predicted surface, driving it towards the region of greatest uncertainty. Finally, we experimentally compare the planner with alternatives in simulation, and demonstrate the complete approach on a real robot. We show that our planner successfully traverses the object, and that the full object shape can be recovered with a good degree of accuracy.

KW - Shape modeling

KW - Tactile exploration

U2 - 10.1142/S0219843618500147

DO - 10.1142/S0219843618500147

M3 - Article

VL - 15

JO - International Journal of Humanoid Robotics

JF - International Journal of Humanoid Robotics

SN - 0219-8436

M1 - 1850014

ER -