Pose and Motion from Contact

Jia, Y-B.; Erdmann, Michael

doi:10.1177/02783649922066349

Cited by 29 publications

(17 citation statements)

References 15 publications

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“…The closest work to this paper is by Moll and Erdmann [9] who explored the problem of reconstructing both the shape and pose of a convex smooth object by rolling it in between two planar palms. Previously, Jia and Erdmann [10] had constructed observers to estimate the pose and predict the motion of an object dynamically pushed.…”

Section: Introductionmentioning

confidence: 99%

Shape and pose recovery from planar pushing

Leonard

Rodríguez

2015

2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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Abstract-Tactile exploration refers to the use of physical interaction to infer object properties. In this work, we study the feasibility of recovering the shape and pose of a movable object from observing a series of contacts. In particular, we approach the problem of estimating the shape and trajectory of a planar object lying on a frictional surface, and being pushed by a frictional probe. The probe, when in contact with the object, makes observations of the location of contact and the contact normal.Our approach draws inspiration from the SLAM problem, where noisy observations of the location of landmarks are used to reconstruct and locate a static environment. In tactile exploration, analogously, we can think of the object as a rigid but moving environment, and of the pusher as a sensor that reports contact points on the boundary of the object.A key challenge to tactile exploration is that, unlike visual feedback, sensing by touch is intrusive in nature. The object moves by the action of sensing. In the 2D version of the problem that we study in this paper, the well understood mechanics of planar frictional pushing provides a motion model that plays the role of odometry. The conjecture we investigate in this paper is whether the models of frictional pushing are sufficiently descriptive to simultaneously estimate the shape and pose of an object from the cumulative effect of a sequence of pushes.

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Section: Introductionmentioning

confidence: 99%

Shape and pose recovery from planar pushing

Leonard

Rodríguez

2015

2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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“…Prior research on pose estimation for contact manipulation has focused on using simple analytical motion models derived from the physics of pushing to build analytical state estimators to track the pose of the object from contact positions on the hand [1].…”

Section: Introductionmentioning

confidence: 99%

Pose estimation for contact manipulation with manifold particle filters

Koval

Doğar

Pollard

et al. 2013

2013 IEEE/RSJ International Conference on Intelligent Robots and Systems

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Abstract-We investigate the problem of estimating the state of an object during manipulation. Contact sensors provide valuable information about the object state during actions which involve persistent contact, e.g. pushing. However, contact sensing is very discriminative by nature, and therefore the set of object states which contact a sensor constitutes a lowerdimensional manifold in the state space of the object. This causes stochastic state estimation methods such as particle filters to perform poorly when contact sensors are used. We propose a new algorithm, the manifold particle filter, which uses dual particles directly sampled from the contact manifold to avoid this problem. The algorithm adapts to the probability of contact by dynamically changing the number of dual particles sampled from the manifold. We compare our algorithm to the particle filter through extensive experiments and we show that our algorithm is both faster and better at estimating the state. Our algorithm's performance improves with increasing sensor accuracy and the filter's update rate. We implement the algorithm on a real robot using a force/torque sensor and strain gauges to track the pose of a pushed object.

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“…Early work included approaches based on fitting a parameterizable object model to contact points [8], using observability theory to estimate object pose [9], and using active tactile interaction to explore objects [10]. More recent work uses Bayesian estimation.…”

Section: A Related Workmentioning

confidence: 99%

Localization and manipulation of small parts using GelSight tactile sensing

Li¹,

Platt

Yuan³

et al. 2014

2014 IEEE/RSJ International Conference on Intelligent Robots and Systems

206

178

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Abstract-Robust manipulation and insertion of small parts can be challenging because of the small tolerances typically involved. The key to robust control of these kinds of manipulation interactions is accurate tracking and control of the parts involved. Typically, this is accomplished using visual servoing or force-based control. However, these approaches have drawbacks. Instead, we propose a new approach that uses tactile sensing to accurately localize the pose of a part grasped in the robot hand. Using a feature-based matching technique in conjunction with a newly developed tactile sensing technology known as GelSight that has much higher resolution than competing methods, we synthesize high-resolution height maps of object surfaces. As a result of these high-resolution tactile maps, we are able to localize small parts held in a robot hand very accurately. We quantify localization accuracy in benchtop experiments and experimentally demonstrate the practicality of the approach in the context of a small parts insertion problem.

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Pose and Motion from Contact

Cited by 29 publications

References 15 publications

Shape and pose recovery from planar pushing

Shape and pose recovery from planar pushing

Pose estimation for contact manipulation with manifold particle filters

Localization and manipulation of small parts using GelSight tactile sensing

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