Time Integrating Articulated Body Dynamics Using Position-Based Collocation Method

Pan, Zherong; Manocha, Dinesh

doi:10.29007/v4ch

Cited by 17 publications

(2 citation statements)

References 27 publications

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“…However, this decreases the accuracy and can result in unstable behavior where objects have unreal-istically large accelerations. To generate stable behaviour at large time-step sizes, Pan and Manocha [29] propose an integrator for articulated body dynamics by using only position variables to formulate the dynamic equation. Moreover, Fan et al [10] propose linear-time variational integrators of arbitrarily high order for robotic simulation and use them in trajectory optimization to complete robotics tasks.…”

Section: Related Workmentioning

confidence: 99%

Parareal with a learned coarse model for robotic manipulation

Agboh

Grainger

Ruprecht

et al. 2020

Comput. Visual Sci.

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A key component of many robotics model-based planning and control algorithms is physics predictions, that is, forecasting a sequence of states given an initial state and a sequence of controls. This process is slow and a major computational bottleneck for robotics planning algorithms. Parallel-in-time integration methods can help to leverage parallel computing to accelerate physics predictions and thus planning. The Parareal algorithm iterates between a coarse serial integrator and a fine parallel integrator. A key challenge is to devise a coarse model that is computationally cheap but accurate enough for Parareal to converge quickly. Here, we investigate the use of a deep neural network physics model as a coarse model for Parareal in the context of robotic manipulation. In simulated experiments using the physics engine Mujoco as fine propagator we show that the learned coarse model leads to faster Parareal convergence than a coarse physics-based model. We further show that the learned coarse model allows to apply Parareal to scenarios with multiple objects, where the physics-based coarse model is not applicable. Finally, we conduct experiments on a real robot and show that Parareal predictions are close to real-world physics predictions for robotic pushing of multiple objects. Code (https://doi.org/10.5281/zenodo.3779085) and videos (https://youtu.be/wCh2o1rf-gA) are publicly available.

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Section: Related Workmentioning

confidence: 99%

Parareal with a learned coarse model for robotic manipulation

Agboh

Grainger

Ruprecht

et al. 2020

Comput. Visual Sci.

View full text Add to dashboard Cite

show abstract

“…We can make physics engines faster by using larger simulation time steps, however this decreases the accuracy and can quickly result in unstable behavior. To generate stable behaviour at large time-step sizes, Pan and Manocha [30] propose an integrator for articulated body dynamics by using only position variables to formulate the dynamic equation. Moreover, Fan et al [9] propose linear-time variational integrators of arbitrarily high order for robotic simulation and use them in trajectory optimization to complete robotics tasks.…”

Section: Related Workmentioning

confidence: 99%

Combining Coarse and Fine Physics for Manipulation using Parallel-in-Time Integration

Agboh,

Ruprecht,

Dogar

2019

Preprint

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We present a method for fast and accurate physics-based predictions during non-prehensile manipulation planning and control. Given an initial state and a sequence of controls, the problem of predicting the resulting sequence of states is a key component of a variety of model-based planning and control algorithms. We propose combining a coarse (i.e. computationally cheap but not very accurate) predictive physics model, with a fine (i.e. computationally expensive but accurate) predictive physics model, to generate a hybrid model that is at the required speed and accuracy for a given manipulation task. Our approach is based on the Parareal algorithm, a parallel-in-time integration method used for computing numerical solutions for general systems of ordinary differential equations. We adapt Parareal to combine a coarse pushing model with an off-the-shelf physics engine to deliver physics-based predictions that are as accurate as the physics engine but run in substantially less wall-clock time, thanks to parallelization across time. We use these physics-based predictions in a model-predictive-control framework based on trajectory optimization, to plan pushing actions that avoid an obstacle and reach a goal location. We show that with hybrid physics models, we can achieve the same success rates as the planner that uses the offthe-shelf physics engine directly, but significantly faster. We present experiments in simulation and on a real robotic setup. Videos are available here: https://youtu.be/5e9oTeu4JOU.

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