Learning modular and transferable forward models of the motions of push manipulated objects

Kopicki, Marek; Zurek, Sebastian; Stolkin, Rustam; Moerwald, Thomas; Wyatt, Jeremy L.

doi:10.1007/s10514-016-9571-3

Cited by 38 publications

(38 citation statements)

References 51 publications

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“…First, they formulated the problem as regression and subsequently as density estimation. In Kopicki et al (2017) they extended this work further. Their architecture is modular in that multiple object-and context-specific forward models are learned which represent different constraints on the object's motion.…”

Section: Metrically Precise Modelsmentioning

confidence: 88%

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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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Section: Metrically Precise Modelsmentioning

confidence: 88%

“…In contrast, metrically precise models have investigated how to learn a mapping between actions and its effects, e.g. (Kopicki et al, 2017). A second more recent strand is the application of deep learning techniques to learn a physical intuition of the mechanics of pushing from visual data, see Fragkiadaki et al (2015).…”

Section: Final Remarksmentioning

confidence: 99%

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

2020

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“…Even if this is solved, approximations used in rigid body engines can render poor predictions. An alternative is to learn a model from data [1], [11], [18], [26], or to use a hybrid approach [3], [32]. Learning methods divide into data-intensive and data-efficient methods.…”

Section: A Learning Models For Pushingmentioning

confidence: 99%

“…There are also data-efficient approaches to learning push effects [2], [18], [19]. These models typically use hand-crafted features, and have not yet been used for push planning.…”

Section: A Learning Models For Pushingmentioning

confidence: 99%

Uncertainty averse pushing with model predictive path integral control

Arruda

Mathew

Kopicki

et al. 2017

2017 IEEE-RAS 17th International Conference on Humanoid Robotics (Humanoids)

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Planning robust robot manipulation requires good forward models that enable robust plans to be found. This work shows how to achieve this using a forward model learned from robot data to plan push manipulations. We explore learning methods (Gaussian Process Regression, and an Ensemble of Mixture Density Networks) that give estimates of the uncertainty in their predictions. These learned models are utilised by a model predictive path integral (MPPI) controller to plan how to push the box to a goal location. The planner avoids regions of high predictive uncertainty in the forward model. This includes both inherent uncertainty in dynamics, and meta uncertainty due to limited data. Thus, pushing tasks are completed in a robust fashion with respect to estimated uncertainty in the forward model and without the need of differentiable cost functions. We demonstrate the method on a real robot, and show that learning can outperform physics simulation. Using simulation, we also show the ability to plan uncertainty averse paths.

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“…Box pushing. When pushing an unknown object a robot needs to plan its motions and pushing actions while taking model [29], sensing, and actuation uncertainty into account. Therefore, we model the pushing task as a hybrid control continuous state POMDP.…”

Section: Related Workmentioning

confidence: 99%

Hybrid control trajectory optimization under uncertainty

Pajarinen

Kyrki

Koval

et al. 2017

2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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Abstract-Trajectory optimization is a fundamental problem in robotics. While optimization of continuous control trajectories is well developed, many applications require both discrete and continuous, i.e. hybrid controls. Finding an optimal sequence of hybrid controls is challenging due to the exponential explosion of discrete control combinations. Our method, based on Differential Dynamic Programming (DDP), circumvents this problem by incorporating discrete actions inside DDP: we first optimize continuous mixtures of discrete actions, and, subsequently force the mixtures into fully discrete actions. Moreover, we show how our approach can be extended to partially observable Markov decision processes (POMDPs) for trajectory planning under uncertainty. We validate the approach in a car driving problem where the robot has to switch discrete gears and in a box pushing application where the robot can switch the side of the box to push. The pose and the friction parameters of the pushed box are initially unknown and only indirectly observable.

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Learning modular and transferable forward models of the motions of push manipulated objects

Cited by 38 publications

References 51 publications

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

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

Uncertainty averse pushing with model predictive path integral control

Hybrid control trajectory optimization under uncertainty

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