Model-free robot manipulation of doors and drawers by means of fixed-grasps

Karayiannidis, Yiannis; Smith, Christian; Vina, Francisco; Ögren, Petter; Kragić, Danica

doi:10.1109/icra.2013.6631214

Cited by 21 publications

(24 citation statements)

References 14 publications

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“…We have assumed a constant axis of rotation around the fingertips of the hand that might not correspond precisely with the actual axis around which the object rotates when it is manipulated. In order to cope with this issue, we aim to use adaptive control techniques previously used for estimating the kinematic constraints of hinged doors [19] and treat the object as a virtual hinge. We are also interested in coupling this work with probabilistic grasp assessment techniques and object categorization as demonstrated in our previous work in [20], [21].…”

Section: B Experimental Resultsmentioning

confidence: 99%

Predicting slippage and learning manipulation affordances through Gaussian Process regression

Viña

Bekiroglu

Smith

et al. 2013

2013 13th IEEE-RAS International Conference on Humanoid Robots (Humanoids)

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Abstract-Object grasping is commonly followed by some form of object manipulation -either when using the grasped object as a tool or actively changing its position in the hand through in-hand manipulation to afford further interaction. In this process, slippage may occur due to inappropriate contact forces, various types of noise and/or due to the unexpected interaction or collision with the environment.In this paper, we study the problem of identifying continuous bounds on the forces and torques that can be applied on a grasped object before slippage occurs. We model the problem as kinesthetic rather than cutaneous learning given that the measurements originate from a wrist mounted force-torque sensor. Given the continuous output, this regression problem is solved using a Gaussian Process approach.We demonstrate a dual armed humanoid robot that can autonomously learn force and torque bounds and use these to execute actions on objects such as sliding and pushing. We show that the model can be used not only for the detection of maximum allowable forces and torques but also for potentially identifying what types of tasks, denoted as manipulation affordances, a specific grasp configuration allows. The latter can then be used to either avoid specific motions or as a simple step of achieving in-hand manipulation of objects through interaction with the environment.

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Section: B Experimental Resultsmentioning

confidence: 99%

Predicting slippage and learning manipulation affordances through Gaussian Process regression

Viña

Bekiroglu

Smith

et al. 2013

2013 13th IEEE-RAS International Conference on Humanoid Robots (Humanoids)

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“…We showed that, for scenarios where it is reasonable to assume that the grasping point of the end-effector offers some compliance around a rotational axis, the orientation of the grasped object is controllable independently of the end-effector orientation. This is a significant distinction from typical manipulation tasks where a perfectly rigid grasp is assumed [26]- [28].…”

Section: Discussionmentioning

confidence: 99%

Dexterous manipulation with compliant grasps and external contacts

Almeida

Karayiannidis

2017

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

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Abstract-We propose a method that allows for dexterous manipulation of an object by exploiting contact with an external surface. The technique requires a compliant grasp, enabling the motion of the object in the robot hand while allowing for significant contact forces to be present on the external surface. We show that under this type of grasp it is possible to estimate and control the pose of the object with respect to the surface, leveraging the trade-off between force control and manipulative dexterity. The method is independent of the object geometry, relying only on the assumptions of type of grasp and the existence of a contact with a known surface. Furthermore, by adapting the estimated grasp compliance, the method can handle unmodelled effects. The approach is demonstrated and evaluated with experiments on object pose regulation and pivoting against a rigid surface.

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“…Motivated by our recent work for door/drawer opening [17] we propose a method for estimating the human joint position in a simple human-robot co-manipulation scenario in which the human is modeled as a rotational joint. The robot is controlled by a damping-type force controller for safe interaction while a feedforward or a feedback termin case of a leveling task -drives the robot motion along the unconstrained directions as they are estimated online.…”

Section: Discussionmentioning

confidence: 99%

“…A sketched proof of the Theorem can be found in the Appendix while details for the general case of uncertainties can be found in [17]. In this work we mainly focus on experimentally validating that the proposed control method can be used to identify the position of the "human joint" in human-robot object co-manipulation.…”

Section: A Controllermentioning

confidence: 99%