Classification and Kinematic Equivalents of Contact Types for Fingertip-Based Robot Hand Manipulation

Rojas, Nicolás; Dollar, Aaron M.

doi:10.1115/1.4032865

Cited by 14 publications

(5 citation statements)

References 19 publications

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“…Although we have only shown an example usage with underactuated hands, our framework can be applied to more general cases including fully actuated hands. For example, even if the hand configuration can be directly obtained from joint encoders of fully actuated hands, we can instead model the exact contacts between the fingertips and the object using kinematic equivalents (Rojas and Dollar, 2016), for which the joint configurations are unknown, and then use our framework to estimate the exact contact positions to accurately register the object into the hand frame. However, it has to be noted that adapting the proposed approach to fully actuated hands requires additional efforts on manipulation stability maintenance.…”

Section: Discussionmentioning

confidence: 99%

“…However, owing to the complexity in the modeling of highly dynamic hand–object systems, the planning and control of dexterous manipulation still remains challenging (Bütepage et al, 2019; Okamura et al, 2000). To keep the problem tractable, the majority of research works have investigated various subproblems, ranging from contact modeling (Bicchi and Kumar, 2000; Han et al, 1997; Rojas and Dollar, 2016), grasp planning (Bohg et al, 2014; Hang et al, 2017), grasp control and stability maintenance (Li et al, 2014), to finger gaiting and sliding planning (Trinkle and Paul, 1990; Xu et al, 2010), tactile-based contact estimation (Koval et al, 2016), hand–object state estimation (Corcoran and Platt, 2010), etc. While some works have successfully demonstrated in-hand manipulation capabilities, they usually rely on many sensing modalities, precise models of objects, simplified task requirements, or a huge dataset for training (Bütepage et al, 2019; OpenAI et al, 2018; Tahara et al, 2010).…”

Section: Introductionmentioning

confidence: 99%

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Hand–object configuration estimation using particle filters for dexterous in-hand manipulation

Hang

Bircher

Morgan

et al. 2019

The International Journal of Robotics Research

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We consider the problem of in-hand dexterous manipulation with a focus on unknown or uncertain hand–object parameters, such as hand configuration, object pose within hand, and contact positions. In particular, in this work we formulate a generic framework for hand–object configuration estimation using underactuated hands as an example. Owing to the passive reconfigurability and the lack of encoders in the hand’s joints, it is challenging to estimate, plan, and actively control underactuated manipulation. By modeling the grasp constraints, we present a particle filter-based framework to estimate the hand configuration. Specifically, given an arbitrary grasp, we start by sampling a set of hand configuration hypotheses and then randomly manipulate the object within the hand. While observing the object’s movements as evidence using an external camera, which is not necessarily calibrated with the hand frame, our estimator calculates the likelihood of each hypothesis to iteratively estimate the hand configuration. Once converged, the estimator is used to track the hand configuration in real time for future manipulations. Thereafter, we develop an algorithm to precisely plan and control the underactuated manipulation to move the grasped object to desired poses. In contrast to most other dexterous manipulation approaches, our framework does not require any tactile sensing or joint encoders, and can directly operate on any novel objects, without requiring a model of the object a priori. We implemented our framework on both the Yale Model O hand and the Yale T42 hand. The results show that the estimation is accurate for different objects, and that the framework can be easily adapted across different underactuated hand models. In the end, we evaluated our planning and control algorithm with handwriting tasks, and demonstrated the effectiveness of the proposed framework.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Hand–object configuration estimation using particle filters for dexterous in-hand manipulation

Hang

Bircher

Morgan

et al. 2019

The International Journal of Robotics Research

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“…In this approach, the contact condition for a soft fingertip is no longer a point contact with friction with twist, as in the traditional soft finger model, but a segment line with friction. Normally, both point contact with friction and soft finger can be modelled as a revolute joint in the planar case [20]. Thus, in order to deal with the uncertainties due to deformation, it is proposed to model soft fingertip contacts as revolute joints with clearance.…”

Section: Soft Fingertip Modelling For Spatial In-hand Manipulationmentioning

confidence: 99%

In-Hand Manipulation with Soft Fingertips

Sarabandi

Chen

et al. 2022

2022 IEEE 5th International Conference on Soft Robotics (RoboSoft)

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This paper introduces an approach for solving the in-hand manipulation problem, that is, the change of a grasped object pose from an initial configuration to a final one without breaking contact, with robot fingers having out-of-plane motion, redundancy, and equipped with soft fingertips. The proposed technique is based on keeping the initial grasp equilibrium condition as a constraint to resolve finger redundancy. Two important aspects of in-hand manipulation with soft fingertips are then studied; namely: i) the modeling of soft fingertip contacts between fingers and 3D objects, and ii) an efficient method for computing joint angles to move a grasped object between different poses without losing grasp equilibrium. Numerical and empirical experiments using soft fingertips with different shore hardnesses with a two-fingered robot hand, composed of four-degree-of-freedom fingers with out-of-plane motion, are conducted. Results successfully validate the introduced strategy and its components.

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“…2]. The kinematic equivalent of a contact type corresponds to a kinematic constraint which defines the constrained motion between two contacting bodies [14]. For rigid fingertips, the contact model can be assumed as point contact with friction and the kinematic equivalent of that is a revolute joint in the planar case [15].…”

Section: A Modelling Of Soft Fingertipsmentioning

confidence: 99%

On Soft Fingertips for In-Hand Manipulation: Modeling and Implications for Robot Hand Design

Rojas

2019

IEEE Robot. Autom. Lett.

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Contact models for soft fingertips are able to precisely compute deformation when information about contact forces and object position is known, thus improving the traditional soft finger contact model. However, the functionality of these approaches for the study of in-hand manipulation with robot hands has been shown to be limited, since the location of the manipulated object is uncertain due to compliance and closed-loop constraints. This paper presents a novel, tractable approach for contact modelling of soft fingertips in within-hand dexterous manipulation settings. The proposed method is based on a relaxation of the kinematic equivalent of point contact with friction, modelling the interaction between fingertips and objects as joints with clearances rather than ideal instances, and then approximating clearances via affine arithmetic to facilitate computation. These ideas are introduced using planar manipulation to aid discussion, and are used to predict the reachable workspace of a two-fingered robot hand with fingertips of different hardness and geometry. Numerical and empirical experiments are conducted to analyse the effects of soft fingertips on manipulation operability; results demonstrate the functionality of the proposed approach, as well as a tradeoff between hardness and depth in soft fingertips to achieve better manipulation performance of dexterous robot hands.

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Classification and Kinematic Equivalents of Contact Types for Fingertip-Based Robot Hand Manipulation

Abstract: ABSTRACT

Cited by 14 publications

References 19 publications

Hand–object configuration estimation using particle filters for dexterous in-hand manipulation

Hand–object configuration estimation using particle filters for dexterous in-hand manipulation

In-Hand Manipulation with Soft Fingertips

On Soft Fingertips for In-Hand Manipulation: Modeling and Implications for Robot Hand Design

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