The Columbia grasp database

Goldfeder, Corey; Ciocarlie, Matei; Dang, Hao; Allen, Peter K.

doi:10.1109/robot.2009.5152709

Cited by 272 publications

(183 citation statements)

References 21 publications

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“…Although the grasps sampled on shape primitives can be used on a wide range objects, it is still important to seek for other alternative approaches to the parameterization of local geometry and construct our tactile experience database. One option would be utilizing the Columbia Grasp Database (CGDB) [16], which contains over 200,000 stable grasps on about 8,000 object models using several different robot hand models, including a Barrett hand, a PR2 gripper, and a human hand model. Statistically, the stable grasps in the CGDB should sample a wider range of local geometries.…”

Section: Discussionmentioning

confidence: 99%

Grasp adjustment on novel objects using tactile experience from similar local geometry

Dang

Allen

2013

2013 IEEE/RSJ International Conference on Intelligent Robots and Systems

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Abstract-Due to pose uncertainty, merely executing a planned-to-be stable grasp usually results in an unstable grasp in the physical world. In our previous work [1], we proposed a tactile experience based grasping pipeline which utilizes tactile feedback to adjust hand posture during the grasping task of known objects and improves the performance of robotic grasping under pose uncertainty. In this paper, we extend our work to grasp novel objects by utilizing local geometric similarity. To do this, we select a series of shape primitives to parameterize potential local geometries which novel objects may share in common. We then build a tactile experience database that stores information of stable grasps on these local geometries. Using this tactile experience database, our method is able to guide a grasp adjustment process to grasp novel objects around similar local geometries. Experiments indicate that our approach improves the grasping performance on novel objects with similar local geometries under pose uncertainty.

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Section: Discussionmentioning

confidence: 99%

Grasp adjustment on novel objects using tactile experience from similar local geometry

Dang

Allen

2013

2013 IEEE/RSJ International Conference on Intelligent Robots and Systems

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“…We have chosen lanelets since they are as expressive as other formats, such as e.g. OpenDRIVE 7 , yet have a lightweight and extensible representation. Using lanelets allows the road network to be modeled as a directed graph, where each node has four types of outgoing edges: successor, predecessor, adjacentLeft, and adjacentRight (see Fig.…”

Section: A Road Networkmentioning

confidence: 99%

“…[4]- [6]), but none has considered motion planning on roads. Detailed benchmarks have been developed in particular for robotic grasping [7], [8] and for robotic manipulators with a focus on indoor human environments [9]. More abstract benchmark problems for motion planning are provided by the Texas A&M University 1 and by Rice University 2 .…”

Section: Introductionmentioning

confidence: 99%

CommonRoad: Composable benchmarks for motion planning on roads

Althoff

Koschi

Manzinger

2017

2017 IEEE Intelligent Vehicles Symposium (IV)

295

166

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Abstract-Numerical experiments for motion planning of road vehicles require numerous components: vehicle dynamics, a road network, static obstacles, dynamic obstacles and their movement over time, goal regions, a cost function, etc. Providing a description of the numerical experiment precise enough to reproduce it might require several pages of information. Thus, only key aspects are typically described in scientific publications, making it impossible to reproduce results-yet, reproducibility is an important asset of good science. Composable benchmarks for motion planning on roads (CommonRoad) are proposed so that numerical experiments are fully defined by a unique ID; all information required to reconstruct the experiment can be found on the CommonRoad website. Each benchmark is composed by a vehicle model, a cost function, and a scenario (including goals and constraints). The scenarios are partly recorded from real traffic and partly hand-crafted to create dangerous situations. We hope that CommonRoad saves researchers time since one does not have to search for realistic parameters of vehicle dynamics or realistic traffic situations, yet provides the freedom to compose a benchmark that fits one's needs.

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“…Examples include the Columbia Grasp dataset [13]. Such a dataset could be consulted by a robot to determine the optimal grasp.…”

Section: A Big-datamentioning

confidence: 99%

IoT and Robotics: a synergy

Chowdhury¹

2017

Preprint

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Abstract-The Internet of Robotic Things (IoRT) [9] is a concept first introduced by Dan Kara at ABI Research, which talks about augmenting the existing IoT with active sensorization; thereby, opening the doors to novel business ideas, at the intersection of both IoT and Robotics. This position paper considers the synergy between IoT and robotics: it talks about the technologies in IoT that would benefit the robotics domain. The advent of Cloud Robotics and its role in aiding robot functions like sensing, manipulation, and mobility. The paper then discusses the ways in which robots can extend the capabilities of existing IoT infrastructure by acting as a special class of edge device. IoT-aided robotic applications are discussed in various domains like health-care, military, industrial plants and rescue operations. The paper concludes by considering the use case of an Intelligent Transportation System endowed by an IoRT-inspired architecture.

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The Columbia grasp database

Cited by 272 publications

References 21 publications

Grasp adjustment on novel objects using tactile experience from similar local geometry

Grasp adjustment on novel objects using tactile experience from similar local geometry

CommonRoad: Composable benchmarks for motion planning on roads

IoT and Robotics: a synergy

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