The OpenGRASP benchmarking suite: An environment for the comparative analysis of grasping and dexterous manipulation

Ulbrich, Stefan; Kappler, Daniel; Asfour, Tamim; Vahrenkamp, Nikolaus; Bierbaum, Alexander; Przybylski, Markus; Dillmann, Rüdiger

doi:10.1109/iros.2011.6094894

Cited by 36 publications

(28 citation statements)

References 37 publications

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“…The OpenGRASP benchmarking suite [20] presents a simulation framework for robotic manipulation. The benchmarking suite provides test cases and setups, and a standard evaluation scheme for the simulation results.…”

Section: A Robotic Manipulationmentioning

confidence: 99%

Benchmarking in Manipulation Research: Using the Yale-CMU-Berkeley Object and Model Set

Çallı

Walsman

Singh

et al. 2015

IEEE Robot. Automat. Mag.

532

284

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Abstract-In this paper we present the Yale-CMU-Berkeley (YCB) Object and Model set, intended to be used to facilitate benchmarking in robotic manipulation, prosthetic design and rehabilitation research. The objects in the set are designed to cover a wide range of aspects of the manipulation problem; it includes objects of daily life with different shapes, sizes, textures, weight and rigidity, as well as some widely used manipulation tests. The associated database provides highresolution RGBD scans, physical properties, and geometric models of the objects for easy incorporation into manipulation and planning software platforms. In addition to describing the objects and models in the set along with how they were chosen and derived, we provide a framework and a number of example task protocols, laying out how the set can be used to quantitatively evaluate a range of manipulation approaches including planning, learning, mechanical design, control, and many others. A comprehensive literature survey on existing benchmarks and object datasets is also presented and their scope and limitations are discussed. The set will be freely distributed to research groups worldwide at a series of tutorials at robotics conferences, and will be otherwise available at a reasonable purchase cost. It is our hope that the ready availability of this set along with the ground laid in terms of protocol templates will enable the community of manipulation researchers to more easily compare approaches as well as continually evolve benchmarking tests as the field matures.

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Section: A Robotic Manipulationmentioning

confidence: 99%

Benchmarking in Manipulation Research: Using the Yale-CMU-Berkeley Object and Model Set

Çallı

Walsman

Singh

et al. 2015

IEEE Robot. Automat. Mag.

532

284

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“…For instance, concerning the field of grasping manipulation, a software environment for the comparative evaluation of algorithms for grasping and dexterous manipulation is presented in [8]. This tool allows the reproduction of well-defined experiments in real-life scenarios in every laboratory and, hence, provides benchmarks that pave the way for objective comparison and competition in the field of grasping.…”

Section: Related Research Workmentioning

confidence: 99%

A Benchmarking Framework for Control Methods of Maritime Cranes Based on the Functional Mockup Interface

Sanfilippo

Hatledal

Pettersen

et al. 2018

IEEE J. Oceanic Eng.

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A benchmark framework for advanced control methods of maritime cranes is presented based on the use of the Functional Mockup Interface (FMI). The system integrates different manipulator models, all the corresponding hydraulic systems, various vessels, and the surrounding environment for visualisation. Different control methods can be transparently implemented and tested. A set of routine tests, different cost functions and metrics are provided -taking into account several factors, including position accuracy, energy consumption, quality, and safety for both the cranes and the surrounding environment. The concept of operation profiles (OP) is introduced, allowing for definition of different standard transporting and lifting operations. By considering task-oriented routines, this benchmark suite allows the comparison of different control methods independently from the specific crane model to be controlled.Two alternative control methods for maritime cranes based on the use of artificial intelligence (AI) are extensively compared. The first method is based on the use of genetic algorithms (GA), while the second method involves the use of particle swarm optimisation (PSO). Simulation results are presented for both methods.

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“…[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)

298

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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The OpenGRASP benchmarking suite: An environment for the comparative analysis of grasping and dexterous manipulation

Cited by 36 publications

References 37 publications

Benchmarking in Manipulation Research: Using the Yale-CMU-Berkeley Object and Model Set

Benchmarking in Manipulation Research: Using the Yale-CMU-Berkeley Object and Model Set

A Benchmarking Framework for Control Methods of Maritime Cranes Based on the Functional Mockup Interface

CommonRoad: Composable benchmarks for motion planning on roads

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