Predictable behavior during contact simulation: a comparison of selected physics engines

Chung, Se-Joon; Pollard, Nancy S.

doi:10.1002/cav.1712

Cited by 26 publications

(9 citation statements)

References 9 publications

(14 reference statements)

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“…Boeing et al [23] compared PhysX, Bullet, JigLib, Newton, Open Dynamics Engine (ODE), Tokamak and True Axis; they reported that Bullet performed best overall however no physics engine was best at all tasks. Chung et al [24] likewise found when testing Bullet, Dynamic Animation and Robotics Toolkit (DART), MuJoCo, and ODE, that no one engine performed better at all tasks, stating that for different tasks and different conditions a different physics engine was found to be better. These findings are further corroborated by Gonzalez-Badillo et al [25], who showed that PhysX performs better than Bullet for non-complex geometries but is unable to simulate more complex geometries to the same degree as Bullet.…”

Section: B Physics Enginesmentioning

confidence: 98%

Quantifying the Reality Gap in Robotic Manipulation Tasks

Collins

Howard

Leitner

2019

2019 International Conference on Robotics and Automation (ICRA)

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We quantify the accuracy of various simulators compared to a real world robotic reaching and interaction task. Simulators are used in robotics to design solutions for real world hardware without the need for physical access. The 'reality gap' prevents solutions developed or learnt in simulation from performing well, or at at all, when transferred to real-world hardware. Making use of a Kinova robotic manipulator and a motion capture system, we record a ground truth enabling comparisons with various simulators, and present quantitative data for various manipulation-oriented robotic tasks. We show the relative strengths and weaknesses of numerous contemporary simulators, highlighting areas of significant discrepancy, and assisting researchers in the field in their selection of appropriate simulators for their use cases. All code and parameter listings are publicly available from: https://bitbucket.csiro.au/scm/˜col549/quantifyingthe-reality-gap-in-robotic-manipulation-tasks.git.

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Section: B Physics Enginesmentioning

confidence: 98%

Quantifying the Reality Gap in Robotic Manipulation Tasks

Collins

Howard

Leitner

2019

2019 International Conference on Robotics and Automation (ICRA)

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“…For a comparison of physics engines, we refer the reader to two recent studies (Erez et al, 2015 ; Chung and Pollard, 2016 ). Erez et al ( 2015 ) compared ODE, Bullet, PhysX, Havok, and MuJoCo.…”

Section: Analytical Approachesmentioning

confidence: 99%

“…For robotics, this is MuJoCo while gaming engines shine in gaming-related trials, whereby no engine emerges as a clear winner. Chung and Pollard ( 2016 ) compared Bullet, DART, MuJoCo, and ODE with regard to contact simulations whilst focusing on the predictability of behavior. Their main result is that the surveyed engines are sensitive to small changes in initial conditions, emphasizing that parameter tuning is important.…”

Section: Analytical Approachesmentioning

confidence: 99%

Let's Push Things Forward: A Survey on Robot Pushing

2020

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As robot make their way out of factories into human environments, outer space, and beyond, they require the skill to manipulate their environment in multifarious, unforeseeable circumstances. With this regard, pushing is an essential motion primitive that dramatically extends a robot's manipulation repertoire. In this work, we review the robotic pushing literature. While focusing on work concerned with predicting the motion of pushed objects, we also cover relevant applications of pushing for planning and control. Beginning with analytical approaches, under which we also subsume physics engines, we then proceed to discuss work on learning models from data. In doing so, we dedicate a separate section to deep learning approaches which have seen a recent upsurge in the literature. Concluding remarks and further research perspectives are given at the end of the paper. Stüber et al. Let's Push Things Forward PROBLEM STATEMENTEven in ideal conditions, such as structured environments where an agent has a complete model of the environment and perfect sensing abilities, the problems of robotic grasping and manipulation are not trivial. By complete model of the environment we mean that physical and geometric properties of the world, such as pose, shape, friction parameters and the mass of the object we wish to manipulate, are exactly known. In fact, the object to be manipulated is indirectly controlled by contacts with a robot manipulator (e.g. pushing by a contacting finger part), and an inverse model (IM), which computes an action to produce the desired motion or set of forces on the object, may not be known. Sometimes forward models (FM) may be fully or partially known, even where IMs are not available. In such cases, an FM can be used to estimate the next state of a system, given the current state and a set of executable actions. This enables planning to be 4. Physics engines. It employs a physics engine as a "black box" to make predictions about the interactions.5. Data-driven. It learns how to predict physical interaction from examples. 6. Deep learning. As the data-driven approaches, it learns how to construct an FM from examples. The key insight is that the deep learning approaches are based on feature extraction.The features highlighted for each approach are as follows.• The assumptions made by the authors on their approach. We highlight i) the quasi-static assumption in the model, ii) if it is a seminal work on 2D shapes, and iii) if the method required a known model of the object to be manipulated.• The type of motion analysed in the paper, such as 1D, planar (2D translation and 1D rotation around the x−axis), or full 3D (3D translation and 3D rotation).• The aim of the paper. We distinguish between predicting the motion of the object, estimating physical parameters, planning pushes, and analysing a push to reach a stable grasp.

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“…Commonly, test scenes are developed that are designed to assess elements of the physics engine that are likely to deviate from the real world. Such tests evaluate the numerical integration, constraint stability, collision detection and material properties of the physics engine [17], [18], [19]. Tests are evaluated with reference to the real world behaviour by drawing comparisons from the human expectation of the system, or from the derivation of behaviour from first principles.…”

Section: Related Workmentioning

confidence: 99%

Benchmarking Simulated Robotic Manipulation Through a Real World Dataset

Collins

McVicar

Wedlock

et al. 2020

IEEE Robot. Autom. Lett.

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We present a benchmark to facilitate simulated manipulation; an attempt to overcome the obstacles of physical benchmarks through the distribution of a real world, ground truth dataset. Users are given various simulated manipulation tasks with assigned protocols having the objective of replicating the real world results of a recorded dataset. The benchmark comprises of a range of metrics used to characterise the successes of submitted environments whilst providing insight into their deficiencies.We apply our benchmark to two simulation environments, PyBullet and V-Rep, and publish the results. All materials required to benchmark an environment, including protocols and the dataset, can be found at the benchmarks' website 1 .

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Predictable behavior during contact simulation: a comparison of selected physics engines

Cited by 26 publications

References 9 publications

Quantifying the Reality Gap in Robotic Manipulation Tasks

Quantifying the Reality Gap in Robotic Manipulation Tasks

Let's Push Things Forward: A Survey on Robot Pushing

Benchmarking Simulated Robotic Manipulation Through a Real World Dataset

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