An overview of 3D object grasp synthesis algorithms
By A. Sahbani, Sahar El-Khoury, P. Bidaud. Robotics Auton. Syst., 2012
501 citations recorded by the source corpus.
Subfields: Biomedical Engineering, Control and Systems Engineering
View Paper: https://www.semanticscholar.org/paper/a65a5a359ca24077d63e0258eb457a21e5874ccc
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Biomedical Engineering
- Pieter Abbeel
- Sergey Levine
- Gerhard Neumann
- Jan Peters
- Aude Gemma Billard
- Danica Kragic
- Wojciech Zaremba
- Peter Corke
In Control and Systems Engineering
- Sergey Levine
- Pieter Abbeel
- Chelsea Finn
- Jan Peters
- Gerhard Neumann
- Jürgen Leitner
- Wojciech Zaremba
- Peter Corke
Citations in the corpus
- Sample and Feedback Efficient Hierarchical Reinforcement Learning from Human Preferences (2018, background): “Although robotic grasping has been a long-standing research area [1], [2], grasping arbitrary objects is still an open problem.”
- Learning robust, real-time, reactive robotic grasping (2019, background): “Grasping is a canonical problem in robotics and has been investigated for decades, yielding a multitude of different techniques (Bicchi and Kumar, 2000; Bohg et al., 2014; Sahbani et al., 2012; Shimoga, 1996).”
- Data-Driven Grasp Synthesis—A Survey (2013, unknown): “Sahbani et al. [1] divide the data-driven methods based on whether they employ object features or observation of humans during grasping.”
- Helping Robots Learn: A Human-Robot Master-Apprentice Model Using Demonstrations via Virtual Reality Teleoperation (2020, background): “There has been significant work on applying machine learning to object grasping in particular [28], [29].”
- Deep learning for detecting robotic grasps (2013, background): “[53], Bicchi and Kumar [2] and Shimoga [58].”
- Hierarchical Fingertip Space: A Unified Framework for Grasp Planning and In-Hand Grasp Adaptation (2016, background): “The area of robotic grasping includes problems such as grasp stability analysis, grasp synthesis and hand kinematics, object and task representation, grasp adaptation [6], [10], [12] etc.”
- Closing the Loop for Robotic Grasping: A Real-time, Generative Grasp Synthesis Approach (2018, methodology): “Robotic grasping has been investigated for decades, yielding a multitude of different techniques [2, 3, 27, 29].”
- Domain Randomization and Generative Models for Robotic Grasping (2017, methodology): “Grasp planning methods fall into one of two categories: analytical methods and empirical methods [42].”
- GRAB: A Dataset of Whole-Body Human Grasping of Objects (2020, background): “Second, there is significant interest in training models to grasp 3D objects [59].”