Haptic exploration for 3D shape reconstruction using five-finger hands

Bierbaum, Alexander; Welke, Kai; Burger, David; Asfour, Tamim; Dillmann, Rüdiger

doi:10.1109/ichr.2007.4813935

Cited by 11 publications

(5 citation statements)

References 22 publications

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“…Those include a framework for getting volumetric object models through haptic exploration using a contour-following strategy by a human or robotic hand in [7] and [8], grasping based on visual information using additional haptic feedback in a cluttered environment with a robot gripper and a fivefinger anthropomorphic hand [9], and a recent experiencebased grasping approach by [10] which uses an initial blind grasping attempt before querying a database with stable grasps for adjustments.…”

Section: Related Workmentioning

confidence: 99%

Changing pre-grasp strategies with increasing object location uncertainty

Illing

Asfour

Pollard

2014

2014 IEEE/RSJ International Conference on Intelligent Robots and Systems

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Successful and robust grasping for humanoid robots is still an ongoing research topic in robotics. Applying human-inspired grasping strategies does not only correspond with more natural looking motions but can also yield good results regarding task success when having to deal with uncertainty. This study investigates human high-level grasping strategies and how they tend to change for different objects when the uncertainty of object location or orientation increases in between two grasps. We are especially interested in potential gains for humanoid robots in a common household setting. By analyzing collected data from human subject grasp experiments with a set of typical objects found in people's homes, we get better insight into how humans handle uncertainty, as well as when and how they change their applied pre-grasp strategy. By adapting the by far most often observed change from a direct grasp attempt to a tapping strategy when dealing with high uncertainty, we can demonstrate a substantial increase of grasp success rate for our robot system with a Shadow Dexterous Hand mounted on a Motoman SDA10 robot while using less than two hand correction steps on average.

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Section: Related Workmentioning

confidence: 99%

Changing pre-grasp strategies with increasing object location uncertainty

Illing

Asfour

Pollard

2014

2014 IEEE/RSJ International Conference on Intelligent Robots and Systems

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“…The most common approaches for single contact tactile object recognition are voxel based or point clouds [14], [15], [16]. Recently, a very efficient and accurate combination of both was developed [17], which is able to model the object shape and the uncertainty about occupied space.…”

Section: Single Contact Tactile Recognitionmentioning

confidence: 99%

Bayesian tactile object recognition: Learning and recognising objects using a new inexpensive tactile sensor

Corradi

Hall

Iravani

2015

2015 IEEE International Conference on Robotics and Automation (ICRA)

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We present a Bayesian approach to tactile object recognition that improves on state-of-the-art in using singletouch events in two ways. First by improving recognition accuracy from about 90% to about 95%, using about half the number of touches. Second by reducing the number of touches needed for training from about 200 to about 60. In addition, we use a new tactile sensor that is less than one tenth of the cost of widely available sensors. The paper describes the sensor, the likelihood function used with the Naive Bayes classifier, and experiments on a set of ten real objects. We also provide preliminary results to test our approach for its ability to generalise to previously unencountered objects.

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“…This leads to sparse information and prohibitively long data collection times. Early works have used offline methods to collect dense tactile data and used shape-fitting primitives such as superquadratics [13]. Aggregating contact points into a point cloud is often used to represent the shape of the objects.…”

Section: Introductionmentioning

confidence: 99%

Touch if it's Transparent! ACTOR: Active Tactile-Based Category-Level Transparent Object Reconstruction

Murali,

Porr,

Kaboli

2023

2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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Accurate shape reconstruction of transparent objects is a challenging task due to their non-Lambertian surfaces and yet necessary for robots for accurate pose perception and safe manipulation. As vision-based sensing can produce erroneous measurements for transparent objects, the tactile modality is not sensitive to object transparency and can be used for reconstructing the object's shape. We propose AC-TOR, a novel framework for ACtive tactile-based category-level Transparent Object Reconstruction. ACTOR leverages large datasets of synthetic object with our proposed self-supervised learning approach for object shape reconstruction as the collection of real-world tactile data is prohibitively expensive. ACTOR can be used during inference with tactile data from category-level unknown transparent objects for reconstruction. Furthermore, we propose an active-tactile object exploration strategy as probing every part of the object surface can be sample inefficient. We also demonstrate tactile-based categorylevel object pose estimation task using ACTOR. We perform an extensive evaluation of our proposed methodology with real-world robotic experiments with comprehensive comparison studies with state-of-the-art approaches. Our proposed method outperforms these approaches in terms of tactile-based object reconstruction and object pose estimation.

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Haptic exploration for 3D shape reconstruction using five-finger hands

Cited by 11 publications

References 22 publications

Changing pre-grasp strategies with increasing object location uncertainty

Changing pre-grasp strategies with increasing object location uncertainty

Bayesian tactile object recognition: Learning and recognising objects using a new inexpensive tactile sensor

Touch if it's Transparent! ACTOR: Active Tactile-Based Category-Level Transparent Object Reconstruction

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