Soft Tactile Contour Following for Robot-Assisted Wiping and Bathing

Huang, Isabella; Chow, Dylan; Bajcsy, Růžena

doi:10.1109/iros47612.2022.9982071

Cited by 9 publications

(6 citation statements)

References 18 publications

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“…While this is a promising development, it emphasizes positional tracking and overlooks the importance of force modulation, which is especially crucial for tasks such as pat drying. Integrating hardware and sensing, Huang et al [18] pair an inflatable end-effector with a depth camera, achieving both position and force tracking, thereby addressing one of the limitations of [12]. However, similar to [12,27,31], it does not employ any perception skills to detect transparent, wet, and soapy areas common to bed bathing scenarios.…”

Section: Bathing Assistance Systemsmentioning

confidence: 99%

“…While there has been commendable progress in robotics, including manipulating deformable objects like towels [34,39,55], interpreting body poses of individuals on beds [32,33], and ensuring unobtrusive movement around a human [20], several gaps remain. Most notably, existing robot-assisted bed bathing approaches [9,18,52,57] fall short when it comes to implementing caregiving practices that are endorsed and recommended by clinical experts, such as clinical nurse specialists and occupational therapists (OTs). For instance, research conducted by [18,27] purely focused on clearing debris but did not account for commonly performed tasks during bed bathing, such as applying soap or drying wet regions of the body.…”

Section: Introductionmentioning

confidence: 99%

“…Most notably, existing robot-assisted bed bathing approaches [9,18,52,57] fall short when it comes to implementing caregiving practices that are endorsed and recommended by clinical experts, such as clinical nurse specialists and occupational therapists (OTs). For instance, research conducted by [18,27] purely focused on clearing debris but did not account for commonly performed tasks during bed bathing, such as applying soap or drying wet regions of the body.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

RABBIT: A Robot-Assisted Bed Bathing System with Multimodal Perception and Integrated Compliance

Madan,

Valdez,

Kim

et al. 2024

Proceedings of the 2024 ACM/IEEE International Conference on Human-Robot Interaction

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This paper introduces RABBIT, a novel robot-assisted bed bathing system designed to address the growing need for assistive technologies in personal hygiene tasks. It combines multimodal perception and dual (software and hardware) compliance to perform safe and comfortable physical human-robot interaction. Using RGB and thermal imaging to segment dry, soapy, and wet skin regions accurately, RABBIT can effectively execute washing, rinsing, and drying tasks in line with expert caregiving practices. Our system includes custom-designed motion primitives inspired by human caregiving techniques, and a novel compliant end-effector called Scrubby, optimized for gentle and effective interactions. We conducted a user study with 12 participants, including one participant with severe mobility limitations, demonstrating the system's effectiveness and perceived comfort. Supplementary material and videos can be found on our website emprise.cs.cornell.edu/rabbit. CCS CONCEPTS• Human-centered computing → Accessibility technologies;• Computer systems organization → Robotics; • Social and professional topics → People with disabilities.

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Section: Bathing Assistance Systemsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

RABBIT: A Robot-Assisted Bed Bathing System with Multimodal Perception and Integrated Compliance

Madan,

Valdez,

Kim

et al. 2024

Proceedings of the 2024 ACM/IEEE International Conference on Human-Robot Interaction

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“…This tactile saliency can assist tactile robots in achieving complex tasks in unstructured environments. For example, a robot may need to explore an object's contour [5]- [8] in clutter using touch. In such scenarios, the identification of tactile saliency is critical since the robot may lack full knowledge of the object and its surrounding stimuli.…”

Section: Related Workmentioning

confidence: 99%

“…This has resulted in a limited understanding of how to achieve robust tactile control in unstructured environments, where unexpected stimuli can impair the accuracy of controllers or policies relying on tactile sensing (as shown in Fig. 1b), making it difficult to achieve precise control in tactile-oriented tasks such as contour following and tactile exploration [5]- [8]. Therefore, it is crucial to develop a methodology that can effectively distinguish between target YL was supported by a UoB-CSC-joint scholarship.…”

Section: Introductionmentioning

confidence: 99%

Tactile Gym 2.0: Sim-to-Real Deep Reinforcement Learning for Comparing Low-Cost High-Resolution Robot Touch

Lin

Lloyd

Church

et al. 2022

IEEE Robot. Autom. Lett.

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High-resolution tactile sensing can provide accurate information about local contact in contact-rich robotic tasks. However, the deployment of such tasks in unstructured environments remains under-investigated. To improve the robustness of tactile robot control in unstructured environments, we propose and study a new concept: tactile saliency for robot touch, inspired by the human touch attention mechanism from neuroscience and the visual saliency prediction problem from computer vision. In analogy to visual saliency, this concept involves identifying key information in tactile images captured by a tactile sensor. While visual saliency datasets are commonly annotated by humans, manually labelling tactile images is challenging due to their counterintuitive patterns. To address this challenge, we propose a novel approach comprised of three interrelated networks: 1) a Contact Depth Network (ConDepNet), which generates a contact depth map to localize deformation in a real tactile image that contains target and noise features; 2) a Tactile Saliency Network (TacSalNet), which predicts a tactile saliency map to describe the target areas for an input contact depth map; 3) and a Tactile Noise Generator (TacNGen), which generates noise features to train the TacSalNet. Experimental results in contact pose estimation and edge-following in the presence of distractors showcase the accurate prediction of target features from real tactile images. Overall, our tactile saliency prediction approach gives robust sim-to-real tactile control in environments with unknown distractors. Project page: https://sites.google.com/ view/tactile-saliency/.

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Artificial Skin Based on Visuo‐Tactile Sensing for 3D Shape Reconstruction: Material, Method, and Evaluation

Zhang,

Yang,

Sun

et al. 2024

Adv Funct Materials

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Artificial skin has shown great potential in robot perception and human healthcare. It provides multifunctional tactile sensing, including 3D shape reconstruction, contact feedback, and temperature perception, where the 3D reconstruction function is indispensable for dexterous hands in tactile cognition and interaction. Vision‐based tactile sensor (VTS) is an innovative bionic tactile sensor and supports high‐resolution, high‐precision, and high‐density tactile reconstruction compared with electronic tactile sensors. Considering the unique contribution of visuo‐tactile sensing to artificial skin, this review focuses on the 3D reconstruction techniques of the VTS. 3D reconstruction methods are classified into five categories based on sensing modalities, hardware categories, and modeling approaches: 1) photometric stereo, 2) binocular depth calibration, 3) optical flow, 4) deep learning, and 5) ToF (time of flight). In addition, the association and difference of reconstruction methods are analyzed from the hardware perspective, and the development and technological details of 3D reconstruction are summarized. On this basis, the challenges and development direction are discussed. This review can be viewed as a technology guide to provide references for interested researchers. Furthermore, it is expected to promote the extensive application of the VTS in artificial skins.

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Soft Tactile Contour Following for Robot-Assisted Wiping and Bathing

Cited by 9 publications

References 18 publications

RABBIT: A Robot-Assisted Bed Bathing System with Multimodal Perception and Integrated Compliance

RABBIT: A Robot-Assisted Bed Bathing System with Multimodal Perception and Integrated Compliance

Tactile Gym 2.0: Sim-to-Real Deep Reinforcement Learning for Comparing Low-Cost High-Resolution Robot Touch

Artificial Skin Based on Visuo‐Tactile Sensing for 3D Shape Reconstruction: Material, Method, and Evaluation

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