GATMO: A Generalized Approach to Tracking Movable Objects

Gallagher, Garratt; Srinivasa, Siddhartha S.; Bagnell, J. Andrew; Ferguson, Dave

doi:10.1109/robot.2009.5152863

Cited by 22 publications

(20 citation statements)

References 14 publications

(13 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Bootstrap learning for object discovery [11] is similar to the "model" part of our work in that it builds models of unmapped objects, but does not reason about instances. Generalized Approach to Tracking Movable Objects (GATMO) [12] is an approach to tracking the movements of movable objects that is similar to our work in that it models movable objects. Our work however, further tracks every observed instance of the movable objects, thus allowing it to reason about the most likely poses of objects.…”

Section: Related Workmentioning

confidence: 99%

Model-Instance Object Mapping

Biswas

Veloso

2015

RoboCup 2014: Robot World Cup XVIII

View full text Add to dashboard Cite

Abstract. Robot localization and mapping algorithms commonly represent the world as a static map. In reality, human environments consist of many movable objects like doors, chairs and tables. Recognizing that such environment often have a large number of instances of a small number of types of objects, we propose an alternative approach, Model-Instance Object Mapping that reasons about the models of objects distinctly from their different instances. Observations classified as short-term features by Episodic non-Markov Localization are clustered to detect object instances. For each object instance, an occupancy grid is constructed, and compared to every other object instance to build a directed similarity graph. Common object models are discovered as strongly connected components of the graph, and their models as well as distribution of instances saved as the final Model-Instance Object Map. By keeping track of the poses of observed instances of object models, Model-Instance Object Maps learn the most probable locations for commonly observed object models. We present results of Model-Instance Object Mapping over the course of a month in our indoor office environment, and highlight the common object models thus learnt in an unsupervised manner.

show abstract

Section: Related Workmentioning

confidence: 99%

Model-Instance Object Mapping

Biswas

Veloso

2015

RoboCup 2014: Robot World Cup XVIII

View full text Add to dashboard Cite

show abstract

“…These include HERB ) which uses a generalized approach to tracking movable objects (GATMO) (Gallagher et al 2009). Grasping is performed using force closure and caging grasps, and manipulation of these objects are executed open-loop.…”

Section: Related Workmentioning

confidence: 99%

Model-based autonomous system for performing dexterous, human-level manipulation tasks

Hudson

Jain

et al. 2013

Auton Robot

View full text Add to dashboard Cite

This article presents a model based approach to autonomous dexterous manipulation, developed as part of the DARPA Autonomous Robotic Manipulation Software (ARM-S) program. Performing human-level manipulation tasks is achieved through a novel combination of perception in uncertain environments, precise tool use, forceful dualarm planning and control, persistent environmental tracking, and task level verification. Deliberate interaction with the environment is incorporated into planning and control strategies, which, when coupled with world estimation, allows for refinement of models and precise manipulation. The system takes advantage of sensory feedback immediately with little open-loop execution, attempting true autonomous reasoning and multi-step sequencing that adapts in the face of changing and uncertain environments. A tire change scenario utilizing human tools, discussed throughout the article, is used to described the system approach. A second scenario of cutting a wire is also presented, and is used to illustrate system component reuse and generality.

show abstract

“…HERB [3] uses the generalized approach to tracking movable objects (GATMO) [4] during navigation. It then uses known appearance models of previously seen objects to recognize and localize them in indoor environments [5].…”

Section: Related Workmentioning

confidence: 99%

End-to-end dexterous manipulation with deliberate interactive estimation

Hudson

Howard

et al. 2012

2012 IEEE International Conference on Robotics and Automation

View full text Add to dashboard Cite

Abstract-This paper presents a model based approach to autonomous dexterous manipulation, developed as part of the DARPA Autonomous Robotic Manipulation (ARM) program. The developed autonomy system uses robot, object, and environment models to identify and localize objects, and well as plan and execute required manipulation tasks. Deliberate interaction with objects and the environment increases system knowledge about the combined robot and environmental state, enabling high precision tasks such as key insertion to be performed in a consistent framework. This approach has been demonstrated across a wide range of manipulation tasks, and in independent DARPA testing achived the most successfully completed tasks with the fastest average task execution of any evaluated team.

show abstract

GATMO: A Generalized Approach to Tracking Movable Objects

Cited by 22 publications

References 14 publications

Model-Instance Object Mapping

Model-Instance Object Mapping

Model-based autonomous system for performing dexterous, human-level manipulation tasks

End-to-end dexterous manipulation with deliberate interactive estimation

Contact Info

Product

Resources

About