Coinbot: Intelligent Robotic Coin Bag Manipulation Using Artificial Brain

Gonnochenko, Aleksei; Semochkin, Aleksandr; Egorov, Dmitry E.; Statovoy, Dmitrii; Zabihifar, Seyedhassan; Postnikov, Aleksey; Seliverstova, Elena V.; Zaidi, Ali A.; Stemmler, Jayson; Limkrailassiri, Kevin

doi:10.1109/icara51699.2021.9376455

Cited by 5 publications

(7 citation statements)

References 12 publications

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“…The next common task is bag moving [238] (see Figure 8c), which includes lifting the bag, creating displacement and placing the bag at the target position. The third standard task is item unloading, which requires opening the bag and picking out the items.Finally, bag unloading [231] involves the agent unloading a collection of bags from a basket.…”

Section: Bag Manipulationmentioning

confidence: 99%

Data-Driven Robotic Manipulation of Cloth-like Deformable Objects: The Present, Challenges and Future Prospects

Kadi

Terzic

2023

Sensors

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Manipulating cloth-like deformable objects (CDOs) is a long-standing problem in the robotics community. CDOs are flexible (non-rigid) objects that do not show a detectable level of compression strength while two points on the article are pushed towards each other and include objects such as ropes (1D), fabrics (2D) and bags (3D). In general, CDOs’ many degrees of freedom (DoF) introduce severe self-occlusion and complex state–action dynamics as significant obstacles to perception and manipulation systems. These challenges exacerbate existing issues of modern robotic control methods such as imitation learning (IL) and reinforcement learning (RL). This review focuses on the application details of data-driven control methods on four major task families in this domain: cloth shaping, knot tying/untying, dressing and bag manipulation. Furthermore, we identify specific inductive biases in these four domains that present challenges for more general IL and RL algorithms.

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Section: Bag Manipulationmentioning

confidence: 99%

Data-Driven Robotic Manipulation of Cloth-like Deformable Objects: The Present, Challenges and Future Prospects

Kadi

Terzic

2023

Sensors

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“…The third standard task is item unloading (IU), which requires opening the bag and picking the items out. There is another prevalent task called bag unloading (BU) [117] where the agent is asked to unload a collection of bags from a basket.…”

Section: Bag Manipulationmentioning

confidence: 99%

“…Sacks introduce further difficulties compared to bags as they don't present handles. They usually require special end-effectors, such as rollers [117], so the agent can effectively grasp and hold the sack without damaging the sack. Moreover, bags can have different shapes and sizes, and the centre of gravity changes while transporting the bag.…”

Section: Challenges In Bag Manipulationmentioning

confidence: 99%

“…Moreover, bags can have different shapes and sizes, and the centre of gravity changes while transporting the bag. The agent also needs to consider these factors for safe performance [117].…”

Section: Challenges In Bag Manipulationmentioning

confidence: 99%

“…In BU, Kirchheim et al (2008) [187] and Gonnochenko et al (2021) [117] infer the depth of individual sacks from a depth sensor using detection and segmentation techniques.…”

Section: Perception In Bag Manipulationmentioning

confidence: 99%

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Data-Driven Robotic Manipulation of Cloth-like Deformable Objects: Present, Challenges, and Future Prospects

Kadi¹,

Terzic²

2022

Preprint

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Manipulating cloth-like deformable objects (CDOs) is a long-standing problem in the robotics community. CDOs are flexible (non-rigid) objects that do not show a detectable level of com- pression strength while two points on the article are pushed towards each otherand include objects such as ropes (1D), fabrics (2D) and bags (3D). In general, CDOs’ many degrees of freedom (DoF) introduce severe self-occlusion and complex state-action dynamics as significant obstacles for perception and manipulation systems. These challenges exacerbate existing issues of modern robotic control methods such as imitation learning (IL) and reinforcement learning (RL). This review focuses on the application details of data-driven control methods on four major task families in this domain: cloth-shaping, rope manipulation, dressing and bag manip- ulation. Furthermore, we identify specific inductive biases in these four domains that present challenges for more general IL and RL algorithms, and summarise the future direction for the development of the field.

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Segmentation of Shipping Bags in RGB-D Images

Vasileva

Ivanovski

2022

2022 IEEE 5th International Conference on Image Processing Applications and Systems (IPAS)

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Coinbot: Intelligent Robotic Coin Bag Manipulation Using Artificial Brain

Cited by 5 publications

References 12 publications

Data-Driven Robotic Manipulation of Cloth-like Deformable Objects: The Present, Challenges and Future Prospects

Data-Driven Robotic Manipulation of Cloth-like Deformable Objects: The Present, Challenges and Future Prospects

Data-Driven Robotic Manipulation of Cloth-like Deformable Objects: Present, Challenges, and Future Prospects

Segmentation of Shipping Bags in RGB-D Images

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