| 1. |
- Chen, Yiting, et al.
(författare)
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GraspAda: Deep Grasp Adaptation through Domain Transfer
- 2023
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Ingår i: Proceedings - IEEE International Conference on Robotics and Automation. - 1050-4729. - 9798350323658 ; 2023-May
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Konferensbidrag (refereegranskat)abstract
- Learning-based methods for robotic grasping have been shown to yield high performance. However, they rely on expensive-to-acquire and well-labeled datasets. In addition, how to generalize the learned grasping ability across different scenarios is still unsolved. In this paper, we present a novel grasp adaptation strategy to transfer the learned grasping ability to new domains based on visual data using a new grasp feature representation. We present a conditional generative model for visual data transformation. By leveraging the deep feature representational capacity from the well-trained grasp synthesis model, our approach utilizes feature-level contrastive representation learning and adopts adversarial learning on output space. This way we bridge the domain gap between the new domain and the training domain while keeping consistency during the adaptation process. Based on transformed input grasp data via the generator, our trained model can generalize to new domains without any fine-tuning. The proposed method is evaluated on benchmark datasets and based on real robot experiments. The results show that our approach leads to high performance in new scenarios.
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| 2. |
- Dong, Hao, et al.
(författare)
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Learning-Based Dimensionality Reduction for Computing Compact and Effective Local Feature Descriptors
- 2023
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Ingår i: Proceedings - ICRA 2023 : IEEE International Conference on Robotics and Automation - IEEE International Conference on Robotics and Automation. - 1050-4729. - 9798350323658 ; 2023-May, s. 6189-6195
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Konferensbidrag (refereegranskat)abstract
- A distinctive representation of image patches in form of features is a key component of many computer vision and robotics tasks, such as image matching, image retrieval, and visual localization. State-of-the-art descriptors, from hand-crafted descriptors such as SIFT to learned ones such as HardNet, are usually high-dimensional; 128 dimensions or even more. The higher the dimensionality, the larger the memory consumption and computational time for approaches using such descriptors. In this paper, we investigate multi-layer perceptrons (MLPs) to extract low-dimensional but high-quality descriptors. We thoroughly analyze our method in unsuper-vised, self-supervised, and supervised settings, and evaluate the dimensionality reduction results on four representative descriptors. We consider different applications, including visual localization, patch verification, image matching and retrieval. The experiments show that our lightweight MLPs trained using supervised method achieve better dimensionality reduction than PCA. The lower-dimensional descriptors generated by our approach outperform the original higher-dimensional descriptors in downstream tasks, especially for the hand-crafted ones. The code is available at https://github.com/PRBonn/descriptor-dr.
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| 3. |
- Almeida, Diogo, 1991-, et al.
(författare)
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Cooperative Manipulation and Identification of a 2-DOF Articulated Object by a Dual-Arm Robot
- 2018
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Ingår i: 2018 IEEE INTERNATIONAL CONFERENCE ON ROBOTICS AND AUTOMATION (ICRA). - 1050-4729. - 9781538630815 ; , s. 5445-5451
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Konferensbidrag (refereegranskat)abstract
- In this work, we address the dual-arm manipula-tion of a two degrees-of-freedom articulated object that consistsof two rigid links. This can include a linkage constrainedalong two motion directions, or two objects in contact, wherethe contact imposes motion constraints. We formulate theproblem as a cooperative task, which allows the employment ofcoordinated task space frameworks, thus enabling redundancyexploitation by adjusting how the task is shared by the robotarms. In addition, we propose a method that can estimate thejoint location and the direction of the degrees-of-freedom, basedon the contact forces and the motion constraints imposed bythe object. Experimental results demonstrate the performanceof the system in its ability to estimate the two degrees of freedomindependently or simultaneously.
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| 4. |
- Almeida, Diogo, 1991-, et al.
(författare)
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Folding Assembly by Means of Dual-Arm Robotic Manipulation
- 2016
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Ingår i: 2016 IEEE International Conference on Robotics and Automation. - : IEEE conference proceedings. - 1050-4729. - 9781467380263 ; , s. 3987-3993
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Konferensbidrag (refereegranskat)abstract
- In this paper, we consider folding assembly as an assembly primitive suitable for dual-arm robotic assembly, that can be integrated in a higher level assembly strategy. The system composed by two pieces in contact is modelled as an articulated object, connected by a prismatic-revolute joint. Different grasping scenarios were considered in order to model the system, and a simple controller based on feedback linearisation is proposed, using force torque measurements to compute the contact point kinematics. The folding assembly controller has been experimentally tested with two sample parts, in order to showcase folding assembly as a viable assembly primitive.
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| 5. |
- Anoosheh, Asha, et al.
(författare)
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Night-to-day image translation for retrieval-based localization
- 2019
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Ingår i: Proceedings - IEEE International Conference on Robotics and Automation. - 1050-4729. ; 2019-May, s. 5958-5964
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Konferensbidrag (refereegranskat)abstract
- Visual localization is a key step in many robotics pipelines, allowing the robot to (approximately) determine its position and orientation in the world. An efficient and scalable approach to visual localization is to use image retrieval techniques. These approaches identify the image most similar to a query photo in a database of geo-tagged images and approximate the query's pose via the pose of the retrieved database image. However, image retrieval across drastically different illumination conditions, e.g. day and night, is still a problem with unsatisfactory results, even in this age of powerful neural models. This is due to a lack of a suitably diverse dataset with true correspondences to perform end-to-end learning. A recent class of neural models allows for realistic translation of images among visual domains with relatively little training data and, most importantly, without ground-truth pairings.In this paper, we explore the task of accurately localizing images captured from two traversals of the same area in both day and night. We propose ToDayGAN - a modified image-translation model to alter nighttime driving images to a more useful daytime representation. We then compare the daytime and translated night images to obtain a pose estimate for the night image using the known 6-DOF position of the closest day image. Our approach improves localization performance by over 250% compared the current state-of-the-art, in the context of standard metrics in multiple categories.
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| 6. |
- Arslan Waltersson, Gabriel, 1996, et al.
(författare)
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Planning and Control for Cable-routing with Dual-arm Robot
- 2022
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Ingår i: Proceedings - IEEE International Conference on Robotics and Automation. - 1050-4729. ; 2022-May, s. 1046-1052
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Konferensbidrag (refereegranskat)abstract
- In this paper, we propose a new framework for solving cable-routing problems with a dual-arm robot, where the objective is to clip a Deformable Linear Object (DLO) into several arbitrarily placed fixtures. The core of the framework is a task-space planner, which builds a roadmap from predefined tasks and employs a replanning strategy based on a genetic algorithm, if problems occur. The manipulation tasks are executed with either individual or coordinated control of the arms. Moreover, hierarchical quadratic programming is used to solve the inverse differential kinematics together with extra feasibility objectives. A vision system first identifies the desired fixture route and structure preserved registration estimates the state of the DLO in real-time. The framework is tested on real-world experiments with a YuMi robot, demonstrating a 90% success rate for 3 fixture problems.
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| 7. |
- Bekiroglu, Yasemin, et al.
(författare)
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A probabilistic framework for task-oriented grasp stability assessment
- 2013
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Ingår i: 2013 IEEE International Conference on Robotics and Automation (ICRA). - : IEEE Computer Society. - 1050-4729. - 9781467356411 ; , s. 3040-3047
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Konferensbidrag (refereegranskat)abstract
- We present a probabilistic framework for grasp modeling and stability assessment. The framework facilitates assessment of grasp success in a goal-oriented way, taking into account both geometric constraints for task affordances and stability requirements specific for a task. We integrate high-level task information introduced by a teacher in a supervised setting with low-level stability requirements acquired through a robot's self-exploration. The conditional relations between tasks and multiple sensory streams (vision, proprioception and tactile) are modeled using Bayesian networks. The generative modeling approach both allows prediction of grasp success, and provides insights into dependencies between variables and features relevant for object grasping.
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| 8. |
- Bekiroglu, Yasemin, et al.
(författare)
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Integrating Grasp Planning with Online Stability Assessment using Tactile Sensing
- 2011
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Ingår i: IEEE International Conference on Robotics and Automation. - : IEEE conference proceedings. - 1050-4729. - 9781612843865 ; , s. 4750-4755
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Konferensbidrag (refereegranskat)abstract
- This paper presents an integration of grasp planning and online grasp stability assessment based on tactile data. We show how the uncertainty in grasp execution posterior to grasp planning can be dealt with using tactile sensing and machine learning techniques. The majority of the state-of-the-art grasp planners demonstrate impressive results in simulation. However, these results are mostly based on perfect scene/object knowledge allowing for analytical measures to be employed. It is questionable how well these measures can be used in realistic scenarios where the information about the object and robot hand may be incomplete and/or uncertain. Thus, tactile and force-torque sensory information is necessary for successful online grasp stability assessment. We show how a grasp planner can be integrated with a probabilistic technique for grasp stability assessment in order to improve the hypotheses about suitable grasps on different types of objects. Experimental evaluation with a three-fingered robot hand equipped with tactile array sensors shows the feasibility and strength of the integrated approach.
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| 9. |
- Bekiroglu, Yasemin, 1982, et al.
(författare)
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Probabilistic Consolidation of Grasp Experience
- 2016
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Ingår i: Proceedings - IEEE International Conference on Robotics and Automation. - : IEEE conference proceedings. - 1050-4729. ; , s. 193-200
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Konferensbidrag (refereegranskat)abstract
- We present a probabilistic model for joint representation of several sensory modalities and action parameters in a robotic grasping scenario. Our non-linear probabilistic latent variable model encodes relationships between grasp-related parameters, learns the importance of features, and expresses confidence in estimates. The model learns associations between stable and unstable grasps that it experiences during an exploration phase. We demonstrate the applicability of the model for estimating grasp stability, correcting grasps, identifying objects based on tactile imprints and predicting tactile imprints from object-relative gripper poses. We performed experiments on a real platform with both known and novel objects, i.e., objects the robot trained with, and previously unseen objects. Grasp correction had a 75% success rate on known objects, and 73% on new objects. We compared our model to a traditional regression model that succeeded in correcting grasps in only 38% of cases.
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| 10. |
- Bergagård, Patrik, 1984, et al.
(författare)
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Calculating restart states using reset transitions
- 2014
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Ingår i: Proceedings - IEEE International Conference on Robotics and Automation. - 1050-4729. ; , s. 3345-3350
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Konferensbidrag (refereegranskat)abstract
- This paper presents a supervisory control theory based offline approach for calculating restart states in a manufacturing control system. Given these precalculated restart states, an operator can be given instructions for how to correctly resynchronize the control system and the manufacturing resources during the online restart phase, as part of the error recovery process. Restarting from a restart state guarantees that all requirements on the nominal and the restarted productions are fulfilled. The paper includes an empirical comparison showing that the proposed approach enables restart states calculation for systems of sizes that could not be handled using an earlier presented approach.
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