The DressGripper: A Collaborative Gripper With Electromagnetic Fingertips for Dressing Assistance

Dragusanu, Mihai; Marullo, Sara; Malvezzi, Monica; Achilli, Gabriele Maria; Valigi, Maria Cristina; Prattichizzo, Domenico; Salvietti, Gionata

doi:10.1109/lra.2022.3183756

Cited by 13 publications

(6 citation statements)

References 32 publications

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“…To this end, by way of example, we carried out experiments in simulation by using the pneumatic RBO Hand 2. However, magnetic forces can be exploited also to change the behaviour of a tendon-driven hand, as we showed in a preliminary work [43]. When considering soft continuous or tendon-driven hands, what remarkably changes is the way in which forces and torques transmit through the fingers' structure.…”

Section: Discussionmentioning

confidence: 97%

On the Use of Magnets to Robustify the Motion Control of Soft Hands

Marullo

Salvietti

Prattichizzo

2022

IEEE Robot. Autom. Lett.

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In this letter, we propose a physics-based framework to exploit magnets in robotic manipulation. More specifically, we suggest equipping soft and underactuated hands with magnetic elements, which can generate a magnetic actuation able to synergistically interact with tendon-driven and pneumatic actuations, engendering a complementarity that enriches the capabilities of the actuation system. Magnetic elements can act as additional Degrees of Actuation (DoAs), robustifying the motion control of the device and augmenting the hand manipulation capabilities. We investigate the interaction of a soft hand with itself for enriching possible hand shaping, and the interaction of the hand with the environment for enriching possible grasping capabilities. Physics laws and notions reported in the manuscript can be used as a guidance for DoAs augmentation and can provide tools for the design of novel soft hands.

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Section: Discussionmentioning

confidence: 97%

On the Use of Magnets to Robustify the Motion Control of Soft Hands

Marullo

Salvietti

Prattichizzo

2022

IEEE Robot. Autom. Lett.

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“…Further research in this area is concerned with cloth state modeling [23], safe motion planning [24], [25], grasping point selection [26] and novel gripper design for dressing assistance [27].…”

Section: A Robotic Dressing Assistancementioning

confidence: 99%

Do You Need a Hand? -- a Bimanual Robotic Dressing Assistance Scheme

Zhu¹,

Gienger²,

Franzese³

et al. 2023

Preprint

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Developing physically assistive robots capable of dressing assistance has the potential to significantly improve the lives of the elderly and disabled population. However, most robotics dressing strategies considered a single robot only, which greatly limited the performance of the dressing assistance. In fact, healthcare professionals perform the task bimanually. Inspired by them, we propose a bimanual cooperative scheme for robotic dressing assistance. In the scheme, an interactive robot joins hands with the human thus supporting/guiding the human in the dressing process, while the dressing robot performs the dressing task. We identify a key feature that affects the dressing action and propose an optimal strategy for the interactive robot using the feature. A dressing coordinate based on the posture of the arm is defined to better encode the dressing policy. We validate the interactive dressing scheme with extensive experiments and also an ablation study. The experiment video is available on https://sites.google.com/view/bimanualassitdressing/home

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“…Conversely, cloth manipulation, particularly with a safety focus, has seen limited exploration. Recent work primarily adopts learning-based approaches for safe manipulation policies [34], [35] and learning to ensure safe interactions between cloth and its environment [36], [37]. Notably, Erickson et al [38] demonstrated using learned models for safe model predictive control.…”

Section: B Safe Manipulationmentioning

confidence: 99%

Traffic-Aware Autonomous Driving with Differentiable Traffic Simulation

Zheng

Son

2023

2023 IEEE International Conference on Robotics and Automation (ICRA)

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Cloth manipulation is a category of deformable object manipulation of great interest to the robotics community, from applications of automated laundry-folding and home organizing and cleaning to textiles and flexible manufacturing. Despite the desire for automated cloth manipulation, the thinshell dynamics and under-actuation nature of cloth present significant challenges for robots to effectively interact with them. Many recent works omit explicit modeling in favor of learning-based methods that may yield control policies directly. However, these methods require large training sets that must be collected and curated. In this regard, we create a framework for differentiable modeling of cloth dynamics leveraging an Extended Position-based Dynamics (XPBD) algorithm. Together with the desired control objective, physics-aware regularization terms are designed for better results, including trajectory smoothness and elastic potential energy. In addition, safety constraints, such as avoiding obstacles, can be specified using signed distance functions (SDFs). We formulate the cloth manipulation task with safety constraints as a constrained optimization problem, which can be effectively solved by mainstream gradient-based optimizers thanks to the end-toend differentiability of our framework. Finally, we assess the proposed framework for manipulation tasks with various safety thresholds and demonstrate the feasibility of result trajectories on a surgical robot. The effects of the regularization terms are analyzed in an additional ablation study.

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The DressGripper: A Collaborative Gripper With Electromagnetic Fingertips for Dressing Assistance

Cited by 13 publications

References 32 publications

On the Use of Magnets to Robustify the Motion Control of Soft Hands

On the Use of Magnets to Robustify the Motion Control of Soft Hands

Do You Need a Hand? -- a Bimanual Robotic Dressing Assistance Scheme

Traffic-Aware Autonomous Driving with Differentiable Traffic Simulation

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