Deep Movement Primitives: Toward Breast Cancer Examination Robot

Sanni, Oluwatoyin; Bonvicini, Giorgio; Khan, Muhammad Arshad; López-Custodio, P. C.; Nazari, Kiyanoush; E., Amir M. Ghalamzan

doi:10.1609/aaai.v36i11.21472

Cited by 8 publications

(1 citation statement)

References 26 publications

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“…The method combines visual and tactile feedback for the robust autonomous grasping of food items. A breast cancer examination robot controller was studied in [236] by a novel LfD technique using deep probabilistic movement primitives. The human demonstration data consist of reach to palpate and palpation trajectories, and the model can generalise to unseen breast poses.…”

Section: Learning From Demonstrationmentioning

confidence: 99%

Tactile-Sensing Technologies: Trends, Challenges and Outlook in Agri-Food Manipulation

Mandil,

Rajendran,

Nazari

et al. 2023

Sensors

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Tactile sensing plays a pivotal role in achieving precise physical manipulation tasks and extracting vital physical features. This comprehensive review paper presents an in-depth overview of the growing research on tactile-sensing technologies, encompassing state-of-the-art techniques, future prospects, and current limitations. The paper focuses on tactile hardware, algorithmic complexities, and the distinct features offered by each sensor. This paper has a special emphasis on agri-food manipulation and relevant tactile-sensing technologies. It highlights key areas in agri-food manipulation, including robotic harvesting, food item manipulation, and feature evaluation, such as fruit ripeness assessment, along with the emerging field of kitchen robotics. Through this interdisciplinary exploration, we aim to inspire researchers, engineers, and practitioners to harness the power of tactile-sensing technology for transformative advancements in agri-food robotics. By providing a comprehensive understanding of the current landscape and future prospects, this review paper serves as a valuable resource for driving progress in the field of tactile sensing and its application in agri-food systems.

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Section: Learning From Demonstrationmentioning

confidence: 99%

Tactile-Sensing Technologies: Trends, Challenges and Outlook in Agri-Food Manipulation

Mandil,

Rajendran,

Nazari

et al. 2023

Sensors

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Modular autonomous strawberry picking robotic system

Parsa

Debnath

Khan

et al. 2023

Journal of Field Robotics

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Challenges in strawberry picking made selective harvesting robotic technology very demanding. However, the selective harvesting of strawberries is a complicated robotic task forming a few scientific research questions. Most available solutions only deal with a specific picking scenario, for example, picking only a single variety of fruit in isolation. Nonetheless, most economically viable (e.g., high‐yielding and/or disease‐resistant) varieties of strawberry are grown in dense clusters. The current perception technology in such use cases is inefficient. In this work, we developed a novel system capable of harvesting strawberries with several unique features. These features allow the system to deal with very complex picking scenarios, for example, dense clusters. Our concept of a modular system makes our system reconfigurable to adapt to different picking scenarios. We designed, manufactured, and tested a patented picking head with 2.5‐degrees of freedom (two independent mechanisms and one dependent cutting system) capable of removing possible occlusions and harvesting the targeted strawberry without any contact with the fruit flesh to avoid damage and bruising. In addition, we developed a novel perception system to localize strawberries and detect their key points, picking points, and determine their ripeness. For this purpose, we introduced two new data sets. Finally, we tested the system in a commercial strawberry growing field and our research farm with three different strawberry varieties. The results show the effectiveness and reliability of the proposed system. The designed picking head was able to remove occlusions and harvest strawberries effectively. The perception system was able to detect and determine the ripeness of strawberries with 95% accuracy. In total, the system was able to harvest 87% of all detected strawberries with a success rate of 83% for all pluckable fruits. We also discuss a series of open research questions in the discussion section.

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Towards autonomous selective harvesting: A review of robot perception, robot design, motion planning and control

Rajendran

Debnath

Mghames

et al. 2023

Journal of Field Robotics

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This paper provides an overview of the current state‐of‐the‐art in selective harvesting robots (SHRs) and their potential for addressing the challenges of global food production. SHRs have the potential to increase productivity, reduce labor costs, and minimize wastage by selectively harvesting only ripe fruits and vegetables. The paper discusses the main components of SHRs, including perception, grasping, cutting, motion planning, and control. It also highlights the challenges in developing SHR technologies, particularly in the areas of robot design, motion planning, and control. The paper also discusses the potential benefits of integrating artificial intelligence and soft robots and data‐driven methods to enhance the performance and robustness of SHR systems. Finally, the paper identifies several open research questions in the field and highlights the need for further research and development efforts to advance SHR technologies to meet the challenges of global food production. Overall, this paper provides a starting point for researchers and practitioners interested in developing SHRs and highlights the need for more research in this field.

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Deep Movement Primitives: Toward Breast Cancer Examination Robot

Cited by 8 publications

References 26 publications

Tactile-Sensing Technologies: Trends, Challenges and Outlook in Agri-Food Manipulation

Tactile-Sensing Technologies: Trends, Challenges and Outlook in Agri-Food Manipulation

Modular autonomous strawberry picking robotic system

Towards autonomous selective harvesting: A review of robot perception, robot design, motion planning and control

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