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1. Modeling Grasp Type Improves Learning-Based Grasp Planning

   https://doi.org/10.1109/LRA.2019.2893410  

   Lu, Qingkai; Hermans, Tucker (April 2019, IEEE Robotics and Automation Letters)
2. Failing to Grasp our Failure to Grasp Automation Failure

   https://doi.org/10.1177/15553434241228799  

   Chiou, Erin K (January 2024, Journal of Cognitive Engineering and Decision Making)

   This paper discusses three points inspired by Skraaning and Jamieson’s perspective on automation failure: (a) the limitations of the automation failure concept with expanding system boundaries; (b) parallels between the failure to grasp automation failure and the failure to grasp trust in automation; (c) benefits of taking a pluralistic approach to definitions in sociotechnical systems science. While a taxonomy of automation-involved failures may not directly improve our understanding of how to prevent those failures, it could be instrumental for identifying hazards during test and evaluation of operational systems. 

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3. Edge Grasp Network: A Graph-Based SE(3)-invariant Approach to Grasp Detection

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4. Workspace Aware Online Grasp Planning

   https://doi.org/10.1109/IROS.2018.8593644  

   Akinola, Iretiayo; Varley, Jacob; Chen, Boyuan; Allen, Peter K. (October 2018, Proceedings Article published Oct 2018 in 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS))

   This work provides a framework for a workspace aware online grasp planner. This framework greatly improves the performance of standard online grasp planning algorithms by incorporating a notion of reachability into the online grasp planning process. Offline, a database of hundreds of thousands of unique end-effector poses were queried for feasibility. At runtime, our grasp planner uses this database to bias the hand towards reachable end-effector configurations. The bias keeps the grasp planner in accessible regions of the planning scene so that the resulting grasps are tailored to the situation at hand. This results in a higher percentage of reachable grasps, a higher percentage of successful grasp executions, and a reduced planning time. We also present experimental results using simulated and real environments. 

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5. Multi-Fingered Active Grasp Learning

   https://doi.org/10.1109/IROS45743.2020.9340783  

   Lu, Qingkai; Van der Merwe, Mark; Hermans, Tucker (October 2020, IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS))

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