Pregrasp Manipulation as Trajectory Optimization

King, Jennifer E.; Klingensmith, Matthew; Dellin, Christopher M.; Doğar, Mehmet R.; Velagapudi, Prasanna; Pollard, Nancy S.; Srinivasa, Siddhartha S.

doi:10.15607/rss.2013.ix.015

Cited by 39 publications

(21 citation statements)

References 25 publications

(35 reference statements)

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“…Their main result is that the surveyed engines are sensitive to small changes in initial conditions, emphasising that parameter tuning is important. Another evaluation of MuJoCo was carried out by Kolbert et al (2017) who evaluated the contact model of MuJoCo with regard to predicting the motions and forces involved in three in-hand robotic manipulation primitives, among them pushing. In the course, they also evaluated the contact model proposed by Chavan-Dafle and Rodriguez (2015).…”

Section: Physics Enginesmentioning

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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Section: Physics Enginesmentioning

confidence: 99%

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

2020

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“…In the context of object grasping, the dimension of the configuration is typically reduced by imposing various motion constraints relative to transfer and transit actions [16]. It is also often common to explicitly introduce intermediate actions, such as reconfiguration, for cost and computation efficiency [17]. Our method is related to the probabilistic path planning method for multiple robots with subdimensional expansion [18], wherein plans in each individual robots configuration space are obtained separately, then those spaces are entangled when robots come into close proximity with one another.…”

Section: Related Workmentioning

confidence: 99%

“…Path π main can be followed simultaneously by a train of other objects lined up behind the frontal object if they have a similar or smaller footprint. To this end, the algorithm proceeds into placing the remaining objects in the reverse order of list L (lines [8][9][10][11][12][13][14][15][16][17][18][19][20][21][22]. Note that L can also contain a single object as a special case.…”

Section: B Nested Pushingmentioning

confidence: 99%

Object Rearrangement with Nested Nonprehensile Manipulation Actions

Song

Boularias

2019

2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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This paper considers the problem of rearrangement planning, i.e finding a sequence of manipulation actions that displace multiple objects from an initial configuration to a given goal configuration. Rearrangement is a critical skill for robots so that they can effectively operate in confined spaces that contain clutter. Examples of tasks that require rearrangement include packing objects inside a bin, wherein objects need to lay according to a predefined pattern. In tight bins, collision-free grasps are often unavailable. Nonprehensile actions, such as pushing and sliding, are preferred because they can be performed using minimalistic end-effectors that can easily be inserted in the bin. Rearrangement with nonprehensile actions is a challenging problem as it requires reasoning about object interactions in a combinatorially large configuration space of multiple objects. This work revisits several existing rearrangement planning techniques and introduces a new one that exploits nested nonprehensile actions by pushing several similar objects simultaneously along the same path, which removes the need to rearrange each object individually. Experiments in simulation and using a real Kuka robotic arm show the ability of the proposed approach to solve difficult rearrangement tasks while reducing the length of the end-effector's trajectories.

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“…Recently, there has been interest in generating push trajectories using sampling based planners (Lau et al 2011, Cosgun et al 2011, trajectory optimization (King et al 2013), and learning methods (Zito et al 2012). We leverage this work by using the quasistatic physics model (Lynch et al 1992, Howe & Cutkosky 1996, the same model used by much of this prior work (Dogar & Srinivasa 2010, Dogar & Srinivasa 2011, to estimate the motion of the object.…”

Section: Manipulation Via Pushingmentioning

confidence: 99%

Pose estimation for planar contact manipulation with manifold particle filters

Koval

Pollard

Srinivasa

2015

The International Journal of Robotics Research

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We investigate the problem of using contact sensors to estimate the pose of an object during planar pushing by a fixed-shape hand. Contact sensors are unique because they inherently discriminate between "contact" and "no-contact" configurations. As a result, the set of object configurations that activates a sensor constitutes a lower-dimensional contact manifold in the configuration space of the object. This causes conventional state estimation methods, such as the particle filter, to perform poorly during periods of contact due to particle starvation.In this paper, we introduce the manifold particle filter as a principled way of solving the state estimation problem when the state moves between multiple manifolds of different dimensionality. The manifold particle filter avoids particle starvation during contact by adaptively sampling particles that reside on the contact manifold from the dual proposal distribution. We describe three techniques-one analytical, and two sample-based-of sampling from the dual proposal distribution and compare their relative strengths and weaknesses. We present simulation results that show that all three techniques outperform the conventional particle filter in both speed and accuracy. Additionally, we implement the manifold particle filter on a real robot and show that it successfully tracks the pose of a pushed object using commercially available tactile sensors.

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Pregrasp Manipulation as Trajectory Optimization

Cited by 39 publications

References 25 publications

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

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

Object Rearrangement with Nested Nonprehensile Manipulation Actions

Pose estimation for planar contact manipulation with manifold particle filters

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