Geometric reachability analysis for grasp planning in cluttered scenes for varying end-effectors

Azizi, Vahid; Kimmel, Andrew; Bekris, Kostas E.; Kapadia, Mubbasir

doi:10.1109/coase.2017.8256196

Cited by 12 publications

(10 citation statements)

References 21 publications

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“…This work provides a system that autonomously generates data to train CNNs for object detection and pose estimation in robotic setups. Object detection and pose estimation are tasks that are frequently required before grasping [38] or rearranging objects with a robot [39], [40]. A key feature of the proposed system is physical reasoning.…”

Section: Discussionmentioning

confidence: 99%

A self-supervised learning system for object detection using physics simulation and multi-view pose estimation

Mitash

Bekris

Boularias

2017

2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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Progress has been achieved recently in object detection given advancements in deep learning. Nevertheless, such tools typically require a large amount of training data and significant manual effort to label objects. This limits their applicability in robotics, where solutions must scale to a large number of objects and variety of conditions. This work proposes an autonomous process for training a Convolutional Neural Network (CNN) for object detection and pose estimation in robotic setups. The focus is on detecting objects placed in cluttered, tight environments, such as a shelf with multiple objects. In particular, given access to 3D object models, several aspects of the environment are physically simulated. The models are placed in physically realistic poses with respect to their environment to generate a labeled synthetic dataset. To further improve object detection, the network self-trains over real images that are labeled using a robust multi-view pose estimation process. The proposed training process is evaluated on several existing datasets and on a dataset collected for this paper with a Motoman robotic arm. Results show that the proposed approach outperforms popular training processes relying on synthetic -but not physically realistic -data and manual annotation. The key contributions are the incorporation of physical reasoning in the synthetic data generation process and the automation of the annotation process over real images.

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Section: Discussionmentioning

confidence: 99%

A self-supervised learning system for object detection using physics simulation and multi-view pose estimation

Mitash

Bekris

Boularias

2017

2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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“…For the experiments, the size of R ee was 25, 000 vertices, and the maximum number of maneuvers κ was set to 20. For CHOMP each (2), Motoman+Vacuum (3,4) environment had a signed-distance field constructed for it, and also had its parameters tuned per benchmark, since no single set of parameters sufficed for all. For CHOMP and IK-CBiRRT, reachable, and collision-free arm configurations were computed through IK at goal poses.…”

Section: Methodsmentioning

confidence: 99%

“…Many existing methods are capable of finding collision-free motions in such tabletop challenges [1][2] [3]. Prior work [4] has shown that large amounts of clutter can significantly reduce the viable valid grasps on an object. While traditional strategies successfully find solutions in the tabletop setting, more cluttered environments like the shelf results in a massive degradation of performance, as highlighted in Fig.…”

Section: Introductionmentioning

confidence: 99%

Fast, Anytime Motion Planning for Prehensile Manipulation in Clutter

Kimmel

Shome

Littlefield

et al. 2018

2018 IEEE-RAS 18th International Conference on Humanoid Robots (Humanoids)

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Many methods have been developed for planning the motion of robotic arms for picking and placing, ranging from local optimization to global search techniques, which are effective for sparsely placed objects. Dense clutter, however, still adversely affects the success rate, computation times, and quality of solutions in many real-world setups. The current work integrates tools from existing methodologies and proposes a framework that achieves high success ratio in clutter with anytime performance. The idea is to first explore the lower dimensional end effector's task space efficiently by ignoring the arm, and build a discrete approximation of a navigation function, which guides the end effector towards the set of available grasps or object placements. This is performed online, without prior knowledge of the scene. Then, an informed sampling-based planner for the entire arm uses Jacobianbased steering to reach promising end effector poses given the task space guidance. While informed, the method is also comprehensive and allows the exploration of alternative paths over time if the task space guidance does not lead to a solution. This paper evaluates the proposed method against alternatives in picking or placing tasks among varying amounts of clutter for a variety of robotic manipulators with different end-effectors. The results suggest that the method reliably provides higher quality solution paths quicker, with a higher success rate relative to alternatives.

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“…In some applications, one important constraint that needs to be considered relates to avoiding collisions with objects in the scene. This goal could be achieved, as shown in [41], with a motion constraint graph that allows the identification of object surfaces that are reachable by the gripper. This method, however, does not provide information about the grasping quality.…”

Section: Grasping In Constrained Environmentsmentioning

confidence: 99%

Optimal Grasping Pose Synthesis in a Constrained Environment

et al. 2020

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In the last few decades, several approaches have been presented to accomplish tasks with robots or autonomous systems in a glovebox; nevertheless, in nuclear facilities, risky operations are still executed by humans that guarantee a high manipulation capability and dexterity. Inside the gloveboxes, robotic devices have to operate in cluttered environments, or environments with limited space for movement; therefore, it is of significant interest to identify grasping poses that are feasible within such constrained environments. In this paper, we present and experimentally evaluate a strategy to synthesise optimal grasps considering geometric primitives for a manipulation systems in a constrained environment. The novel strategy has been experimentally evaluated in a cluttered environment (as a glovebox mock-up) with realistic objects, and the efficacy of the proposed grasping algorithm is proposed.

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Geometric reachability analysis for grasp planning in cluttered scenes for varying end-effectors

Cited by 12 publications

References 21 publications

A self-supervised learning system for object detection using physics simulation and multi-view pose estimation

A self-supervised learning system for object detection using physics simulation and multi-view pose estimation

Fast, Anytime Motion Planning for Prehensile Manipulation in Clutter

Optimal Grasping Pose Synthesis in a Constrained Environment

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