| 1. |
- Almeida, Diogo, 1991-, et al.
(författare)
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Team KTH’s Picking Solution for the Amazon Picking Challenge 2016
- 2020
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Ingår i: Advances on Robotic Item Picking: Applications in Warehousing and E-Commerce Fulfillment. - Cham : Springer Nature. ; , s. 53-62
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Bokkapitel (övrigt vetenskapligt/konstnärligt)abstract
- In this chapter we summarize the solution developed by team KTH for the Amazon Picking Challenge 2016 in Leipzig, Germany. The competition, which simulated a warehouse automation scenario, was divided into two parts: a picking task, where the robot picks items from a shelf and places them into a tote, and a stowing task, where the robot picks items from a tote and places them in a shelf. We describe our approach to the problem starting with a high-level overview of the system, delving later into the details of our perception pipeline and strategy for manipulation and grasping. The hardware platform used in our solution consists of a Baxter robot equipped with multiple vision sensors.
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| 2. |
- Antonova, Rika, et al.
(författare)
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Sequential Topological Representations for Predictive Models of Deformable Objects
- 2021
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Ingår i: Proceedings of the 3rd Conference on Learning for Dynamics and Control, L4DC 2021. - : ML Research Press. ; , s. 348-360
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Konferensbidrag (refereegranskat)abstract
- Deformable objects present a formidable challenge for robotic manipulation due to the lack of canonical low-dimensional representations and the difficulty of capturing, predicting, and controlling such objects. We construct compact topological representations to capture the state of highly deformable objects that are topologically nontrivial. We develop an approach that tracks the evolution of this topological state through time. Under several mild assumptions, we prove that the topology of the scene and its evolution can be recovered from point clouds representing the scene. Our further contribution is a method to learn predictive models that take a sequence of past point cloud observations as input and predict a sequence of topological states, conditioned on target/future control actions. Our experiments with highly deformable objects in simulation show that the proposed multistep predictive models yield more precise results than those obtained from computational topology libraries. These models can leverage patterns inferred across various objects and offer fast multistep predictions suitable for real-time applications.
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| 3. |
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| 4. |
- Pinto Basto de Carvalho, Joao Frederico, 1988-, et al.
(författare)
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Long-term Prediction of Motion Trajectories Using Path Homology Clusters
- 2019
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Ingår i: 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). - : Institute of Electrical and Electronics Engineers (IEEE).
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Konferensbidrag (refereegranskat)abstract
- In order for robots to share their workspace with people, they need to reason about human motion efficiently. In this work we leverage large datasets of paths in order to infer local models that are able to perform long-term predictions of human motion. Further, since our method is based on simple dynamics, it is conceptually simple to understand and allows one to interpret the predictions produced, as well as to extract a cost function that can be used for planning. The main difference between our method and similar systems, is that we employ a map of the space and translate the motion of groups of paths into vector fields on that map. We test our method on synthetic data and show its performance on the Edinburgh forum pedestrian long-term tracking dataset [1] where we were able to outperform a Gaussian Mixture Model tasked with extracting dynamics from the paths.
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| 5. |
- Pinto Basto de Carvalho, Joao Frederico, 1988-
(författare)
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Topological Methods for Motion Prediction and Caging
- 2020
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- To fulfill the requirements of automation in unstructured environmentsit will be necessary to endow robots with the ability to plan actions thatcan handle the dynamic nature of changing environments and are robust toperceptual errors. This thesis focuses on the design of algorithms to facilitatemotion planning in human environments and rigid object manipulation.Understanding human motion is a necessary first step to be able to performmotion planning in spaces that are inhabited by humans. Specifically throughlong-term prediction a robot should be able to plan collision-avoiding paths tocarry out whatever tasks are required of it. In this thesis we present a methodto classify motions by clustering paths, together with a method to translatethe resulting clusters into motion patterns that can be used to predict motion.Another challenge of robotics is the manipulation of everyday objects.Even in the realm of rigid objects, safe object-manipulation by either grippersor dexterous robotic hands requires complex physical parameter estimation.Such estimations are often error-prone and misestimations may cause completefailure to execute the desired task. Caging is presented as an alternativeapproach to classical manipulation by employing topological invariants todetermine whether an object is secured with only bounded mobility. Wepresent a method to decide whether a rigid object is in fact caged by a givengrasp or not, relying only on a rough approximation of the object and thegripper.
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| 6. |
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| 7. |
- Varava, Anastasiia, et al.
(författare)
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Free space of rigid objects : caging, path non-existence, and narrow passage detection
- 2020
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Ingår i: The international journal of robotics research. - : SAGE Publications Inc.. - 0278-3649 .- 1741-3176.
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Tidskriftsartikel (refereegranskat)abstract
- In this work, we propose algorithms to explicitly construct a conservative estimate of the configuration spaces of rigid objects in two and three dimensions. Our approach is able to detect compact path components and narrow passages in configuration space which are important for applications in robotic manipulation and path planning. Moreover, as we demonstrate, they are also applicable to identification of molecular cages in chemistry. Our algorithms are based on a decomposition of the resulting three- and six-dimensional configuration spaces into slices corresponding to a finite sample of fixed orientations in configuration space. We utilize dual diagrams of unions of balls and uniform grids of orientations to approximate the configuration space. Furthermore, we carry out experiments to evaluate the computational efficiency on a set of objects with different geometric features thus demonstrating that our approach is applicable to different object shapes. We investigate the performance of our algorithm by computing increasingly fine-grained approximations of the object’s configuration space. A multithreaded implementation of our approach is shown to result in significant speed improvements.
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| 8. |
- Varava, Anastasiia, et al.
(författare)
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Free Space of Rigid Objects : Caging, Path Non-existence, and Narrow Passage Detection
- 2020
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Ingår i: Springer Proceedings in Advanced Robotics. - Cham : Springer Science and Business Media B.V.. - 2511-1256. ; 14, s. 19-35
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Tidskriftsartikel (refereegranskat)abstract
- In this paper, we present an approach towards approximating configuration spaces of 2D and 3D rigid objects. The approximation can be used to identify caging configurations and establish path non-existence between given pairs of configurations. We prove correctness and analyse completeness of our approach. Using dual diagrams of unions of balls and uniform grids on SO(3), we provide a way to approximate a 6D configuration space of a rigid object. Depending on the desired level of guaranteed approximation accuracy, the experiments with our single core implementation show runtime between 5–21 s and 463–1558 s. Finally, we establish a connection between robotic caging and molecular caging from organic chemistry, and demonstrate that our approach is applicable to 3D molecular models. The supplementary material for this paper can be found at https://anvarava.github.io/publications/wafr-2018-supplementary-material.pdf.
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| 9. |
- Varava, Anastasiia, et al.
(författare)
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Free Space of Rigid Objects: Caging, Path Non-Existence, and Narrow Passage Detection
-
Ingår i: The international journal of robotics research. - 0278-3649 .- 1741-3176.
-
Tidskriftsartikel (refereegranskat)abstract
- In this work we propose algorithms to explicitly construct a conservative estimate of the configuration spaces of rigid objects in 2D and 3D. Our approach is able to detect compact path components and narrow passages in configuration space which are important for applications in robotic manipulation and path planning. Moreover, as we demonstrate, they are also applicable to identification of molecular cages in chemistry. Our algorithms are based on a decomposition of the resulting 3 and 6 dimensional configuration spaces into slices corresponding to a finite sample of fixed orientations in configuration space. We utilize dual diagrams of unions of balls and uniform grids of orientations to approximate the configuration space. We carry out experiments to evaluate the computational efficiency on a set of objects with different geometric features thus demonstrating that our approach is applicable to different object shapes. We investigate the performance of our algorithm by computing increasingly fine-grained approximations of the object's configuration space.
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