A review of the challenges in mobile manipulation: systems design and RoboCup challenges

Sereinig, Martin; Werth, Wolfgang; Faller, Lisa-Marie

doi:10.1007/s00502-020-00823-8

Cited by 23 publications

(11 citation statements)

References 82 publications

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“…Several mobile manipulation benchmarks exist to evaluate the performance of a system [21,149,158]. These benchmarks evaluate the performance of a mobile manipulator in a variety of contexts but fail to take advantage of modern simulators [180] that utilize real-world scanned data of household objects [25] and environments [27,10].…”

Section: Mobile Manipulationmentioning

confidence: 99%

Learning Mobile Manipulation

Watkins¹

2022

Preprint

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Providing mobile robots with the ability to manipulate objects has, despite decades of research, remained a challenging problem. The problem is approachable in constrained environments where there is ample prior knowledge of the environment layout and manipulatable objects. The challenge is in building systems that scale beyond specific situational instances and gracefully operate in novel conditions. In the past, researchers used heuristic and simple rule-based strategies to accomplish tasks such as scene segmentation or reasoning about occlusion. These heuristic strategies work in constrained environments where a roboticist can make simplifying assumptions about everything from the geometries of the objects to be interacted with, level of clutter, camera position, lighting, and a myriad of other relevant variables. The work in this thesis will demonstrate how to build a system for robotic mobile manipulation that is robust to changes in these variables. This robustness will be enabled by recent simultaneous advances in the fields of big data, deep learning, and simulation. The ability of simulators to create realistic sensory data enables the generation of massive corpora of labeled training data for various grasping and navigation-based tasks. It is now possible to build systems that work in the real world trained using deep learning entirely on synthetic data. The ability to train and test on synthetic data allows for quick iterative development of new perception, planning and grasp execution algorithms that work in many environments.To build a robust system, this thesis introduces a novel multiple-view shape reconstruction architecture that leverages unregistered views of the object. To navigate to objects without localizing the agent, this thesis introduces a novel panoramic target goal architecture that takes previous views of the agent to inform a policy to navigate through an environment. Additionally, a novel next-best-view methodology is introduced to allow the agent to move around the object and refine its initial understanding of the object. The results show that this deep learned sim-to-real approach performs best when compared to heuristic-based methods in terms of reconstruction quality and success-weighted-by-path-length (SPL). This approach is also adaptable to the environment and robot chosen due to its modular design.I would like to acknowledge all members of the Columbia Robotics Lab (CRLab) for making my stay there an enjoyable and rewarding experience. In particular, I would like to thank

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Section: Mobile Manipulationmentioning

confidence: 99%

Learning Mobile Manipulation

Watkins¹

2022

Preprint

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“…Także w konstrukcji mechanicznej manipulatorów można zauważyć nowe rozwiązania. Ramiona mani-pulatorów montowane na ruchomych platformach mobilnych wykazują wysoki stosunek udźwigu do masy własnej, co zostało osiągnięte w niektórych najnowszych manipulatorach, takich jak KUKA LWR IIWA, TM Techman, czy Universal Robots [1]. W robotyce do zadań specjalnych także widoczny jest postęp w osiąganiu najwyższego wskaźnika udźwigu do masy własnej.…”

Section: Andrzej Typiakunclassified

“…Do przeprowadzenia efektywnej optymalizacji topologicznej ważne jest odpowiednie zdefiniowanie obciążeń. W praktyce podczas zdalnego sterowania teleoperowanymi robotami mobilnymi występują nie tylko obciążenia związane z przenoszeniem ładunków czy używaniem końcówki roboczej [1], ale też inne obciążenia, które należy uwzględnić w założeniach. Manipulator robota mobilnego do zadań specjalnych w czasie eksploatacji podlega złożonym obciążeniom.…”

Section: Podział Obciążeń Ze Względu Na Zadaniaunclassified

Selection of Loads in the Design and Optimization of Manipulators of Portable Mobile Robots for Special Applications

Krakówka¹,

Typiak²

2022

PAR

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The construction of mobile robot manipulators for special applications must be optimized to achieve the required working capacity while maintaining a low mass. In the literature on the optimization of manipulator structures, the authors most often take into account the most unfavorable load case in their calculations or take into account several distinguished load cases resulting from the static or dynamic of the loaded manipulator. To optimize the design of a teleoperated robot manipulator, which during operation is exposed to contact with obstacles in an unstructured environment, the load analysis must be carried out in many aspects. The results of such analysis are used to select manipulator elements, e.g. drives, and to determine the loads for the design of the load-bearing structure and its optimization. The determined loads can be used to perform topological optimization of components of manipulators to minimize the mass while maintaining strength adapted to the operating conditions. Preliminary work on the preparation of a method for selecting such loads is presented.

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“…Fortunately, decades of manipulation work has developed an ecosystem of platforms capable of both fine dexterous in-hand-manipulation [e.g., robotic Rubik's cube solver (Yang B. et al, 2020a)] and inexpensive, high-volume pick and place robots (Altuzarra et al, 2011) with ongoing goals to approach human-like manual dexterity (Billard and Kragic, 2019). Manipulation research has primarily focused on stationary arms, but manipulation also occurs on mobile platforms (see relevant reviews on wheeled robots (Thakar et al, 2023) and on RoboCup (Sereinig et al, 2020)). Part of the reason why legged robots have often remained limited to simple interactions with their surroundings is that they are usually deployed in environments that are less predictable, making legged manipulation tasks more challenging.…”

Section: Introductionmentioning

confidence: 99%

Legged robots for object manipulation: A review

et al. 2023

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Legged robots can have a unique role in manipulating objects in dynamic, human-centric, or otherwise inaccessible environments. Although most legged robotics research to date typically focuses on traversing these challenging environments, many legged platform demonstrations have also included “moving an object” as a way of doing tangible work. Legged robots can be designed to manipulate a particular type of object (e.g., a cardboard box, a soccer ball, or a larger piece of furniture), by themselves or collaboratively. The objective of this review is to collect and learn from these examples, to both organize the work done so far in the community and highlight interesting open avenues for future work. This review categorizes existing works into four main manipulation methods: object interactions without grasping, manipulation with walking legs, dedicated non-locomotive arms, and legged teams. Each method has different design and autonomy features, which are illustrated by available examples in the literature. Based on a few simplifying assumptions, we further provide quantitative comparisons for the range of possible relative sizes of the manipulated object with respect to the robot. Taken together, these examples suggest new directions for research in legged robot manipulation, such as multifunctional limbs, terrain modeling, or learning-based control, to support a number of new deployments in challenging indoor/outdoor scenarios in warehouses/construction sites, preserved natural areas, and especially for home robotics.

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A review of the challenges in mobile manipulation: systems design and RoboCup challenges

Cited by 23 publications

References 82 publications

Learning Mobile Manipulation

Learning Mobile Manipulation

Selection of Loads in the Design and Optimization of Manipulators of Portable Mobile Robots for Special Applications

Legged robots for object manipulation: A review

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