A Summary of Team MIT's Approach to the Amazon Picking Challenge 2015

Yu, Kuan-Ting; Fazeli, Nima; Dafle, Nikhil Chavan; Taylor, Orion; Donlon, Elliott; Lankenau, Guillermo Diaz; Rodríguez, Alberto

doi:10.48550/arxiv.1604.03639

Cited by 23 publications

(16 citation statements)

References 9 publications

(7 reference statements)

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“…When planning suction grasps, existing approaches either directly propose grasps on a point cloud of the scene using heuristic methods or sample a range of candidate grasps and rank them using a quality metric. For the former, common approaches are grasping near estimated centroids of flat surfaces [25], grasping along inward surface normals towards an object centroid [10], or pushing objects from the top or side until a suction seal is formed [8]. In the latter case, Domae et al [7] use a geometric model that assesses planarity by convolving a contact template with the image.…”

Section: B Suction Graspingmentioning

confidence: 99%

Mechanical Search on Shelves using a Novel "Bluction" Tool

Huang¹,

Danielczuk²,

Kim³

et al. 2022

Preprint

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Shelves are common in homes, warehouses, and commercial settings due to their storage efficiency. However, this efficiency comes at the cost of reduced visibility and accessibility. When looking from a side (lateral) view of a shelf, most objects will be fully occluded, resulting in a constrained lateralaccess mechanical search problem. To address this problem, we introduce: (1) a novel bluction tool, which combines a thin pushing blade and suction cup gripper, (2) an improved LAX-RAY simulation pipeline and perception model that combines ray-casting with 2D Minkowski sums to efficiently generate target occupancy distributions, and (3) a novel SLAX-RAY search policy, which optimally reduces target object distribution support area using the bluction tool. Experimental data from 2000 simulated shelf trials and 18 trials with a physical Fetch robot equipped with the bluction tool suggest that using suction grasping actions improves the success rate over the highest performing push-only policy by 26% in simulation and 67% in physical environments.

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Section: B Suction Graspingmentioning

confidence: 99%

Mechanical Search on Shelves using a Novel "Bluction" Tool

Huang¹,

Danielczuk²,

Kim³

et al. 2022

Preprint

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“…MaskRCNN is a neural network used for object segmentation [21]. The dual-arm system uses a customised version of MaskRCNN written in Tensorflow 5 . The version of Tensorflow used requires older GPU drivers not supported officially by Ubuntu 18.…”

Section: Maskrcnnmentioning

confidence: 99%

A Methodology for Approaching the Integration of Complex Robotics Systems Illustrated through a Bi-manual Manipulation Case-Study

Triantafyllou¹,

Rodrigues²,

Chaikunsaeng³

et al. 2021

Preprint

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The multidisciplinarity of robotics creates a need for robust integration methodologies that can facilitate the adoption of state-of-the-art research components in an industrial application. Unfortunately, there are no clear, communityaccepted guidelines or standards that define the integration of such components in a single robotic system. In this paper, we propose a methodology that assesses the software components of a candidate system on the basis of the effort required to integrate them and the impact their integration will have on a target system. We demonstrate how this methodology can be applied using an industrial tool packing system as an example. The system integrates a wide range of both in-house and thirdparty research outputs and software components. We prove the effectiveness of our approach by evaluating system performance with an experimental benchmark that assesses the robustness, reliability and operational speed of the system for the given packing task. We also demonstrate how our methodology can be used to predict the amount of integration time required for a component. The proposed integration methodology can be applied to any robotic system to facilitate its transition from the research to an industrial environment.

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“…Since the Amazon Picking Challenge 2015, a variety of approaches to a more general bin-picking problem had been proposed [17]- [19]. In [20], authors discussed their observations and lessons drawn from a survey conducted among participating teams in the challenge.…”

Section: Related Workmentioning

confidence: 99%

Development of a Robotic System for Automated Decaking of 3D-Printed Parts

Nguyen¹,

Adrian²,

Yan³

et al. 2020

Preprint

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With the rapid rise of 3D-printing as a competitive mass manufacturing method, manual "decaking" -i.e. removing the residual powder that sticks to a 3D-printed part -has become a significant bottleneck. Here, we introduce, for the first time to our knowledge, a robotic system for automated decaking of 3D-printed parts. Combining Deep Learning for 3D perception, smart mechanical design, motion planning, and force control for industrial robots, we developed a system that can automatically decake parts in a fast and efficient way. Through a series of decaking experiments performed on parts printed by a Multi Jet Fusion printer, we demonstrated the feasibility of robotic decaking for 3D-printing-based mass manufacturing.

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A Summary of Team MIT's Approach to the Amazon Picking Challenge 2015

Cited by 23 publications

References 9 publications

Mechanical Search on Shelves using a Novel "Bluction" Tool

Mechanical Search on Shelves using a Novel "Bluction" Tool

A Methodology for Approaching the Integration of Complex Robotics Systems Illustrated through a Bi-manual Manipulation Case-Study

Development of a Robotic System for Automated Decaking of 3D-Printed Parts

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