The RUTH Gripper: Systematic Object-Invariant Prehensile In-Hand Manipulation via Reconfigurable Underactuation

Lu, Qiujie; Baron, Nicholas; Clark, Angus B.; Rojas, Nicolás

doi:10.15607/rss.2020.xvi.093

Cited by 6 publications

(9 citation statements)

References 22 publications

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“…This method is based on [12] to extract the features of grippers and objects from their point clouds, such that it can train a Point Set Selection Network (PSSN), which can generate contact points on any object for any gripper. However, the method becomes data-inefficient and cumbersome when applied to multi-grasp grippers such as the Robotiq-3F gripper, the BarrettHand, and the RUTH hand [10]; and does not work on grippers with closed-loop constraints (e.g., the RUTH hand, the integrated linkage-driven hand [11]). These drawbacks are discussed in section II-C.…”

Section: B Visual Data-driven Grasping Methodsmentioning

confidence: 99%

“…Secondly, that the proposed method is more generalizable to novel grippers as it does not rely on model specifications such as a URDF, which may have problems when representing gripper geometries based on closed-loop constraints. Indeed, our realworld experiments, conducted with the RUTH hand [10]a gripper with closed-loop constraints, show that UniGrasp fails while EfficientGrasp achieves a grasping success rate of 83.3%. Thirdly, that the proposed method also outperforms the baseline when considering only grippers without closed-loop constraints (e.g., Robotiq-3F gripper, BarrettHand), showing 9.85% higher accuracy in contact points generation compared to UniGrasp and 3.10% higher grasping success rate in simulation (86.3% EfficientGrasp, 83.2% UniGrasp), which indicates that the proposed method can generate better grasps.…”

Section: Introductionmentioning

confidence: 93%

“…This simplifies the control of the gripper and reduces the effects of actuator error accumulation, however with limitations in performing precision grasps. Multi-finger robot gripper designs with a reconfigurable palm can achieve an even wider range of grasp configurations with high precision [10], [20]- [22]. The reconfigurable palm design allows for finger positions and orientations to be controlled relative to each other.…”

Section: A Grasping Using Different Grippersmentioning

confidence: 99%

“…does not have closedloop kinematic chains). However, the method becomes datainefficient and cumbersome when applied to grippers in which 'most open' and 'most closed' gripper operation modes are ambiguous, as in the Robotiq-3F gripper, the BarrettHand, or the RUTH hand [10]. Moreover, UniGrasp cannot work on grippers with closed-loop constraints (e.g., the RUTH hand, the integrated linkage-driven hand [11]) since a URDF cannot represent mechanisms with this characteristic.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

EfficientGrasp: A Unified Data-Efficient Learning to Grasp Method for Multi-Fingered Robot Hands

Baron²,

Zhang

et al. 2022

IEEE Robot. Autom. Lett.

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Autonomous grasping of novel objects that are previously unseen to a robot is an ongoing challenge in robotic manipulation. In the last decades, many approaches have been presented to address this problem for specific robot hands. The UniGrasp framework, introduced recently, has the ability to generalize to different types of robotic grippers; however, this method does not work on grippers with closed-loop constraints and is data-inefficient when applied to robot hands with multigrasp configurations. In this paper, we present EfficientGrasp, a generalized grasp synthesis and gripper control method that is independent of gripper model specifications. EfficientGrasp utilizes a gripper workspace feature rather than UniGrasp's gripper attribute inputs. This reduces memory use by 81.7% during training and makes it possible to generalize to more types of grippers, such as grippers with closed-loop constraints. The effectiveness of EfficientGrasp is evaluated by conducting object grasping experiments both in simulation and real-world; results show that the proposed method also outperforms UniGrasp when considering only grippers without closed-loop constraints. In these cases, EfficientGrasp shows 9.85% higher accuracy in generating contact points and 3.10% higher grasping success rate in simulation. The real-world experiments are conducted with a gripper with closed-loop constraints, which UniGrasp fails to handle while EfficientGrasp achieves a success rate of 83.3%. The main causes of grasping failures of the proposed method are analyzed, highlighting ways of enhancing grasp performance.

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Section: B Visual Data-driven Grasping Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 93%

Section: A Grasping Using Different Grippersmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

EfficientGrasp: A Unified Data-Efficient Learning to Grasp Method for Multi-Fingered Robot Hands

Baron²,

Zhang

et al. 2022

IEEE Robot. Autom. Lett.

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“…For reconfigurable hands such as that developed by X. Cui et al [6], the tendon length for each finger differs, thus the constant force and tendon length models mentioned previously are not longer suitable. Lu et al [10] provided one solution to this problem by routing the tendon through the fivebar-mechanism based reconfigurable palm, ensuring the length of the tendon is independent of the palm configuration. However, this constant tendon system requires a high actuation force due to the complex routing design.…”

Section: Introductionmentioning

confidence: 99%

A Passively Compliant Idler Mechanism for Underactuated Dexterous Grippers with Dynamic Tendon Routing

Wang

Clark

et al. 2020

Lecture Notes in Computer Science

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In the field of robotic hands, tendon actuation is one of the most common ways to control self-adaptive underactuated fingers thanks to its compact size. Either differential or direct drive mechanisms are usually used in these systems to perform synchronised grasping using a single actuator. However, synchronisation problems arise in underactuated grippers whose position of proximal joints varies with time to perform manipulation operations, as this results in a tendon-driven system with dynamic anchor pulleys. This paper introduces a novel passively complaint idler mechanism to avoid unsynchronisation in grippers with a dynamic multi-tendon routing system, such that adequate grasping contact forces are kept under changes in the proximal joints' positions. A re-configurable palm underactuated dexterous gripper is used as a case study, with the performance of the proposed compliant idler system being evaluated and compared through a contact force analysis during rotation and translation in-hand manipulation tasks. Experiment results clearly demonstrate the ability of the mechanism to synchronise a dynamic tendon routing gripper. A video summarising experiments and findings can be found at https://imperialcollegelondon.box.com/s/ hk58688q2hjnu8dhw7uskr7vi9tqr9r5.

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A Variable Stiffness Gripper with Reconfigurable Finger Joint for Versatile Manipulations

Wang

Gao

et al. 2023

Soft Robotics

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The RUTH Gripper: Systematic Object-Invariant Prehensile In-Hand Manipulation via Reconfigurable Underactuation

Cited by 6 publications

References 22 publications

EfficientGrasp: A Unified Data-Efficient Learning to Grasp Method for Multi-Fingered Robot Hands

EfficientGrasp: A Unified Data-Efficient Learning to Grasp Method for Multi-Fingered Robot Hands

A Passively Compliant Idler Mechanism for Underactuated Dexterous Grippers with Dynamic Tendon Routing

A Variable Stiffness Gripper with Reconfigurable Finger Joint for Versatile Manipulations

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