The YCB object and Model set: Towards common benchmarks for manipulation research

Çallı, Berk; Singh, Arjun; Walsman, Aaron; Srinivasa, Siddhartha S.; Abbeel, Pieter; Dollar, Aaron M.

doi:10.1109/icar.2015.7251504

Cited by 652 publications

(498 citation statements)

References 24 publications

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“…These objects are similar to those presented in Fig. 1, such as the ones presented in [23]. The results of this new experiment rendered a weighted success rate of 71.36%.…”

Section: B the Classifier's Performance Analysissupporting

confidence: 81%

Grasp stability assessment through unsupervised feature learning of tactile images

Cockbum

Roberge

et al. 2017

2017 IEEE International Conference on Robotics and Automation (ICRA)

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Abstract-Grasping tasks have always been challenging for robots, despite recent innovations in vision-based algorithms and object-specific training. If robots are to match human abilities and learn to pick up never-before-seen objects, they must combine vision with tactile sensing. This paper present a novel way to improve robotic grasping: by using tactile sensors and an unsupervised feature-learning approach, a robot can find the common denominators behind successful and failed grasps, and use this knowledge to predict whether a grasp attempt will succeed or fail. This method is promising as it uses only high-level features from two tactile sensors to evaluate grasp quality, and works for the training set as well as for new objects. In total, using a total of 54 different objects, our system recognized grasp failure 83.70% of time.

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“…These objects are similar to those presented in Fig. 1, such as the ones presented in [23]. The results of this new experiment rendered a weighted success rate of 71.36%.…”

Section: B the Classifier's Performance Analysissupporting

confidence: 81%

Grasp stability assessment through unsupervised feature learning of tactile images

Cockbum

Roberge

et al. 2017

2017 IEEE International Conference on Robotics and Automation (ICRA)

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“…Although a subset of the manipulation research community has already converged on a standard set of objects (i.e. the YCB object and model set [2]), a widespread protocol and a system of metrics for properly comparing different pipelines are still missing.…”

Section: Introductionmentioning

confidence: 99%

GRASPA 1.0: GRASPA is a Robot Arm graSping Performance BenchmArk

Bottarel

Vezzani

Pattacini

et al. 2020

IEEE Robot. Autom. Lett.

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The use of benchmarks is a widespread and scientifically meaningful practice to validate performance of different approaches to the same task. In the context of robot grasping the use of common object sets has emerged in recent years, however no dominant protocols and metrics to test grasping pipelines have taken root yet. In this paper, we present version 1.0 of GRASPA, a benchmark to test effectiveness of grasping pipelines on physical robot setups. This approach tackles the complexity of such pipelines by proposing different metrics that account for the features and limits of the test platform. As an example application, we deploy GRASPA on the iCub humanoid robot and use it to benchmark our grasping pipeline. As closing remarks, we discuss how the GRASPA indicators we obtained as outcome can provide insight into how different steps of the pipeline affect the overall grasping performance.

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“…We selected objects of different size, texture and shape from the YCB dataset [5], shown in Fig. 3, as a benchmarking set.…”

Section: Experimental Protocol and Evaluationmentioning

confidence: 99%

“…4) We are the first to extensively validate an in-grasp manipulation planner on a real robot hand. We do so with multiple objects from the YCB dataset [5] and introduce relevant error metrics, paving the way for a unified testing scheme in future works (c.f. Section IV).…”

Section: Introductionmentioning

confidence: 99%

Relaxed-Rigidity Constraints: In-Grasp Manipulation using Purely Kinematic Trajectory Optimization

Sundaralingam

Hermans

2017

Robotics: Science and Systems XIII

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Abstract-This paper proposes a novel approach to performing in-grasp manipulation planning: the problem of moving an object with reference to the palm from an initial pose to a goal pose without breaking or making contacts. Our method to perform in-grasp manipulation uses kinematic trajectory optimization which requires no knowledge of dynamic properties of the object or the robot. We define a cost function that attempts to maintain the initial grasp points, while relaxing the constraint that the contacts between finger and object remain rigid. Hence, we name this new formulation relaxed-rigidity constraints. We implement our approach on an Allegro robot hand and perform experiments on 10 objects from the YCB dataset. However, the implementation would work for any object the robot can grasp. We perform thorough analysis and compare to alternative optimization formulations. Our method reaches the desired object pose with a median position error of 13mm across all of the 500 trials without ever dropping the object.

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The YCB object and Model set: Towards common benchmarks for manipulation research

Cited by 652 publications

References 24 publications

Grasp stability assessment through unsupervised feature learning of tactile images

Grasp stability assessment through unsupervised feature learning of tactile images

GRASPA 1.0: GRASPA is a Robot Arm graSping Performance BenchmArk

Relaxed-Rigidity Constraints: In-Grasp Manipulation using Purely Kinematic Trajectory Optimization

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