A measurement model for tracking hand-object state during dexterous manipulation

Corcoran, Craig; Platt, Robert

doi:10.1109/robot.2010.5509194

Cited by 38 publications

(33 citation statements)

References 4 publications

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“…Corcoran and Platt [11] use particle filtering to model objects based on contacts with a robot hand. The object state is estimated during manipulation by integrating the likelihood of contact measurements over possible contact positions.…”

Section: A Related Workmentioning

confidence: 99%

Learning robot tactile sensing for object manipulation

Chebotar

Kroemer

Peters

2014

2014 IEEE/RSJ International Conference on Intelligent Robots and Systems

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Abstract-Tactile sensing is a fundamental component of object manipulation and tool handling skills. With robots entering unstructured environments, tactile feedback also becomes an important ability for robot manipulation.In this work, we explore how a robot can learn to use tactile sensing in object manipulation tasks. We first address the problem of in-hand object localization and adapt three pose estimation algorithms from computer vision. Second, we employ dynamic motor primitives to learn robot movements from human demonstrations and record desired tactile signal trajectories. Then, we add tactile feedback to the control loop and apply relative entropy policy search to learn the parameters of the tactile coupling. Additionally, we show how the learning of tactile feedback can be performed more efficiently by reducing the dimensionality of the tactile information through spectral clustering and principal component analysis. Our approach is implemented on a real robot, which learns to perform a scraping task with a spatula in an altered environment.

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Section: A Related Workmentioning

confidence: 99%

Learning robot tactile sensing for object manipulation

Chebotar

Kroemer

Peters

2014

2014 IEEE/RSJ International Conference on Intelligent Robots and Systems

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“…Others use a particle filter approach to fit a model to the object pose by collecting measurements from touching the object before grasping it [8], [9]. Hebert et al use a fusion approach with vision, force/torque measurements and proprioception to estimate the position of an object with a known model, held in the end-effector [10].…”

Section: Related Workmentioning

confidence: 99%

“…by using vision and mapping a desired trajectory from the image space to robot space. In this case the feedback control is designed using the contact point position which is however based on estimates obtained by (9) i.e. p := p c .…”

Section: Force/motion Controlmentioning

confidence: 99%

Online contact point estimation for uncalibrated tool use

Karayiannidis

Smith

Vina

et al. 2014

2014 IEEE International Conference on Robotics and Automation (ICRA)

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One of the big challenges for robots working outside of traditional industrial settings is the ability to robustly and flexibly grasp and manipulate tools for various tasks. When a tool is interacting with another object during task execution, several problems arise: a tool can be partially or completely occluded from the robot's view, it can slip or shift in the robot's hand -thus, the robot may lose the information about the exact position of the tool in the hand. Thus, there is a need for online calibration and/or recalibration of the tool. In this paper, we present a model-free online tool-tip calibration method that uses force/torque measurements and an adaptive estimation scheme to estimate the point of contact between a tool and the environment. An adaptive force control component guarantees that interaction forces are limited even before the contact point estimate has converged. We also show how to simultaneously estimate the location and normal direction of the surface being touched by the tool-tip as the contact point is estimated. The stability of the the overall scheme and the convergence of the estimated parameters are theoretically proven and the performance is evaluated in experiments on a real robot.

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“…Different combinations of tactile, force and vision information for locating the handle and opening a door were tested and it was proved that the combination of all three modalities outperformed any other possible arrangement [14]. A particle filter approach was used to estimate a tube's pose using both positive and negative contact information -the knowledge of which fingers are touching the object and which are not [15]. Another approach was to model discrete states that contain the possible combinatorial arrangements between fingers and object surfaces using an hybrid systems estimator, estimating these discrete contact modes as well as continuous state variables -i.e.…”

Section: Introductionmentioning

confidence: 99%

Global estimation of an object’s pose using tactile sensing

et al. 2015

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It is essential for a successful completion of a robot object grasping and manipulation task to accurately sense the manipulated object's pose. Typically, computer vision is used to obtain this information, but it may not be available or be reliable in certain situations. This paper presents a global optimisation method where tactile and force sensing together with the robot's proprioceptive data are used to find an object's pose. This method is used to either improve an estimate of the object's pose given by vision or globally estimating it when no vision is available. Results show that the proposed method consistently improves an initial estimate (e.g. from vision) and is also able to find the object's pose without any prior estimate. To demonstrate the performance of the algorithm, an experiment is carried out where the robot is handed a small object (a pencil) and inserts it into a narrow hole without any use of vision.

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A measurement model for tracking hand-object state during dexterous manipulation

Cited by 38 publications

References 4 publications

Learning robot tactile sensing for object manipulation

Learning robot tactile sensing for object manipulation

Online contact point estimation for uncalibrated tool use

Global estimation of an object’s pose using tactile sensing

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