RobWorkPhysicsEngine: A new dynamic simulation engine for manipulation actions

Thulesen, Thomas Nicky; Petersen, Henrik Gordon

doi:10.1109/icra.2016.7487354

Cited by 5 publications

(4 citation statements)

References 9 publications

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“…Their work can be seen as a simple case of using task-level information to design a fast, low-fidelity simulator. While speed may not be the main focus of past work, results presented were often for isolated joints or a few interacting bodies [13], [14]. Similar to recent work [25], we stress the importance of performing experiments for an entire robot system for evaluating simulators.…”

Section: Related Worksupporting

confidence: 63%

“…Other examples include changing the integration steps used by solvers-while dynamics solvers often use fixed, first-order integration steps, Zapolsky and Drumwright [8] proposed an algorithm with adaptive step sizes, which could lead to faster dynamics simulation. The physics engine introduced by Thulesen et al [13] used second-order integration for greater accuracy. In addition, by modeling contact handling as a convex-optimization problem, a speedup was obtained over classic dynamics solvers, which use a Linear-Complementary Problem formulation.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Task-Informed Fidelity Management for Speeding Up Robotics Simulation

Tallavajhula¹,

Schoisengeier²,

Kim³

et al. 2019

Preprint

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Simulators are an important tool in robotics that is used to develop robot software and generate synthetic data for machine learning algorithms. Faster simulation can result in better software validation and larger amounts of data. Previous efforts for speeding up simulators have been performed at the level of simulator building blocks, and robot systems.Our key insight, motivating this work, is that further speedups can be obtained at the level of the robot task. Building on the observation that not all parts of a scene need to be simulated in high fidelity at all times, our approach is to toggle between high-and low-fidelity states for scene objects in a task-informed manner. Our contribution is a framework for speeding up robot simulation by exploiting task knowledge. The framework is agnostic to the underlying simulator, and preserves simulation fidelity. As a case study, we consider a complex materialhandling task. For the associated simulation, which contains many of the characteristics that make robot simulation slow, we achieve a speedup that can be up to three times faster than high fidelity without compromising on the quality of the results. We also demonstrate that faster simulation allows us to train better policies for performing the task at hand in a short period of time. A video summarizing our contributions can be found at https: //youtu.be/PEzypDyqc3o.

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Section: Related Worksupporting

confidence: 63%

Section: Related Workmentioning

confidence: 99%

Task-Informed Fidelity Management for Speeding Up Robotics Simulation

Tallavajhula¹,

Schoisengeier²,

Kim³

et al. 2019

Preprint

View full text Add to dashboard Cite

show abstract

“…The development of fast and accurate physics engines (a computer software that provides routines for the simulation of physical systems) over the last years, e.g. (Thulesen and Petersen, 2016), and the constant increase of the available computational power makes the use of simulation a feasible alternative to physical testing.…”

Section: Introductionmentioning

confidence: 99%

Compensating Pose Uncertainties through Appropriate Gripper Finger Cutouts

Wolniakowski

Gams

Kiforenko

et al. 2018

Acta Mechanica Et Automatica

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The gripper finger design is a recurring problem in many robotic grasping platforms used in industry. The task of switching the gripper configuration to accommodate for a new batch of objects typically requires engineering expertise, and is a lengthy and costly iterative trial-and-error process. One of the open challenges is the need for the gripper to compensate for uncertainties inherent to the workcell, e.g. due to errors in calibration, inaccurate pose estimation from the vision system, or object deformation. In this paper, we present an analysis of gripper uncertainty compensating capabilities in a sample industrial object grasping scenario for a finger that was designed using an automated simulation-based geometry optimization method (Wolniakowski et al., 2013, 2015). We test the developed gripper with a set of grasps subjected to structured perturbation in a simulation environment and in the real-world setting. We provide a comparison of the data obtained by using both of these approaches. We argue that the strong correspondence observed in results validates the use of dynamic simulation for the gripper finger design and optimization.

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“…In our future work we plan to enhance our suite with compliance, and include more elaborate friction models. Furthermore, in our future experiments we plan to test a new physics engine designed for industrial assembly simulation [30].…”

Section: Discussionmentioning

confidence: 99%

Optimizing grippers for compensating pose uncertainties by dynamic simulation

Wolniakowski

Kramberger

Gams

et al. 2016

2016 IEEE International Conference on Simulation, Modeling, and Programming for Autonomous Robots (SIMPAR)

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Gripper design process is one of the interesting challenges in the context of grasping within industry. Typically, simple parallel-finger grippers, which are easy to install and maintain, are used in platforms for robotic grasping. The context switches in these platforms require frequent exchange of gripper fingers to accommodate grasping of new products, while subjected to numerous constraints, such as workcell uncertainties due to the vision systems used. The design of these fingers consumes the man-hours of experienced engineers, and involves a lot of trial-and-error testing. In our previous work, we have presented a method to automatically compute the optimal finger shapes for defined task contexts in simulation. In this paper, we show the performance of our method in an industrial grasping scenario. We first analyze the uncertainties of the used vision system, which are the major source of grasping error. Then, we perform the experiments, both in simulation and in a real setting. The experiments confirmed the validity of our approach. The computed finger design was employed in a real industrial assembly scenario.

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RobWorkPhysicsEngine: A new dynamic simulation engine for manipulation actions

Cited by 5 publications

References 9 publications

Task-Informed Fidelity Management for Speeding Up Robotics Simulation

Task-Informed Fidelity Management for Speeding Up Robotics Simulation

Compensating Pose Uncertainties through Appropriate Gripper Finger Cutouts

Optimizing grippers for compensating pose uncertainties by dynamic simulation

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