Gross Motion Analysis of Fingertip-Based Within-Hand Manipulation

Rojas, Nicolás; Dollar, Aaron M.

doi:10.1109/tro.2016.2588722

Cited by 14 publications

(11 citation statements)

References 19 publications

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“…They do not perform extensive experiments with objects. Rojas and Dollar (2016) present a method to analyze the kinematic-motion of a hand with respect to a grasped object. This tool could be used to find feasible goal poses for an object without changing the current grasp similar to Hertkorn et al (2013).…”

Section: Related Workmentioning

confidence: 99%

“…The set of experiments consist of moving the object under grasp to a goal pose. Finding feasible desired poses given an initial grasp is a complex problem (Rojas and Dollar, 2016;Hertkorn et al, 2013) and we do not formalize a method to obtain them. Instead, we focus on obtaining trajectories to a reachable pose and not on finding reachable poses.…”

Section: Experimental Protocolmentioning

confidence: 99%

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Relaxed-rigidity constraints: kinematic trajectory optimization and collision avoidance for in-grasp manipulation

Sundaralingam

Hermans

2018

Auton Robot

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This paper proposes a novel approach to performing in-grasp manipulation: the problem of moving an object with reference to the palm from an initial pose to a goal pose without breaking or making contacts. Our method to perform in-grasp manipulation uses kinematic trajectory optimization which requires no knowledge of dynamic properties of the object. We implement our approach on an Allegro robot hand and perform thorough experiments on 10 objects from the YCB dataset. However, the proposed method is general enough to generate motions for most objects the robot can grasp. Experimental result support the feasibillty of its application across a variety of object shapes. We explore the adaptability of our approach to additional task requirements by including collision avoidance and joint space smoothness costs. The grasped object avoids collisions with the environment by the use of a signed distance cost function. We reduce the effects of unmodeled object dynamics by requiring smooth joint trajectories. We additionally compensate for errors encountered during trajectory execution by formulating an object pose feedback controller.

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Section: Related Workmentioning

confidence: 99%

Section: Experimental Protocolmentioning

confidence: 99%

Relaxed-rigidity constraints: kinematic trajectory optimization and collision avoidance for in-grasp manipulation

Sundaralingam

Hermans

2018

Auton Robot

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“…Rojas and Dollar [27] present a method to analyze the kinematic-motion of a hand with respect to a grasped object. This tool could be used to find feasible goal poses for an object without changing the current grasp similar to [16].…”

Section: Related Workmentioning

confidence: 99%

“…Finding feasible desired poses given an initial grasp is a complex problem [16,27] and we do not formalize a method to obtain them. Instead, we focus on obtaining trajectories to a reachable pose and not on finding reachable poses.…”

Section: Experimental Protocol and Evaluationmentioning

confidence: 99%

Relaxed-Rigidity Constraints: In-Grasp Manipulation using Purely Kinematic Trajectory Optimization

Sundaralingam

Hermans

2017

Robotics: Science and Systems XIII

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Abstract-This paper proposes a novel approach to performing in-grasp manipulation planning: the problem of moving an object with reference to the palm from an initial pose to a goal pose without breaking or making contacts. Our method to perform in-grasp manipulation uses kinematic trajectory optimization which requires no knowledge of dynamic properties of the object or the robot. We define a cost function that attempts to maintain the initial grasp points, while relaxing the constraint that the contacts between finger and object remain rigid. Hence, we name this new formulation relaxed-rigidity constraints. We implement our approach on an Allegro robot hand and perform experiments on 10 objects from the YCB dataset. However, the implementation would work for any object the robot can grasp. We perform thorough analysis and compare to alternative optimization formulations. Our method reaches the desired object pose with a median position error of 13mm across all of the 500 trials without ever dropping the object.

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“…The technique basically consists of five steps, namely: 1) selection of the contact model for each fingertip, 2) definition of the resulting closed kinematic chain of the Hand-Object System (HOS), 3) construction of the HOS's graph of kinematic constraints, 4) reduction of the graph to a single edge, and 5) determination of the controllable movements. The interested reader is address to [20] for details of this method. Next we discuss an example of precision manipulation analysis that stands out the relevance of the introduced contact models.…”

Section: Regardless Of the Actuation Schemementioning

confidence: 99%

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

Rojas

Dollar

2016

Journal of Mechanisms and Robotics

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Gross Motion Analysis of Fingertip-Based Within-Hand Manipulation

Cited by 14 publications

References 19 publications

Relaxed-rigidity constraints: kinematic trajectory optimization and collision avoidance for in-grasp manipulation

Relaxed-rigidity constraints: kinematic trajectory optimization and collision avoidance for in-grasp manipulation

Relaxed-Rigidity Constraints: In-Grasp Manipulation using Purely Kinematic Trajectory Optimization

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

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