Robotic grasping and contact: a review

By Antonio Bicchi, Vijay R. Kumar. 2000

1,269 citations recorded by the source corpus.

Subfields: Biomedical Engineering, Control and Systems Engineering, Mechanical Engineering

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Citations in the corpus

Learning Synergies Between Pushing and Grasping with Self-Supervised Deep Reinforcement Learning (2018, background): “for learning the dynamics of pushing [4], [18], many of these works have largely focused on the execution of stable pushes for one object at a time.”
Supersizing self-supervision: Learning to grasp from 50K tries and 700 robot hours (2015, methodology): “Given these training data points, we train a CNN-based classiﬁer which given an input image patch predicts the grasp likelihood for different grasp directions.”
Dexterous Manipulation with Deep Reinforcement Learning: Efficient, General, and Low-Cost (2018, background): “Much of the work on robotic hands has focused on grasping [4], as compared to other dexterous skills [5].”
Deep learning for detecting robotic grasps (2013, methodology): “i=1 h [2](t) i W [3] i )”
Asymmetric Actor Critic for Image-Based Robot Learning (2017, background): “The Picking task is similar to grasping objects [29], [30], however in this work we learn an end-to-end policy that moves to the object, grasps it and moves the grasped object to its desired position.”
The Curious Robot: Learning Visual Representations via Physical Interactions (2016, background): “A comprehensive literature review of this area can be found in [17,18].”
Real-time grasp detection using convolutional neural networks (2014, background): “Significant past work uses 3-D simulations to find good grasps [3] [4] [5] [6] [7].”
Supervision via competition: Robot adversaries for learning tasks (2016, neutral): “Refer to [32], [33] for surveys of prior work.”
Learning to push by grasping: Using multiple tasks for effective learning (2016, background): “For a comprehensive literature review, we direct the readers to [8], [9].”
Robot Learning in Homes: Improving Generalization and Reducing Dataset Bias (2018, methodology): “Grasping is one of the fundamental problems in robotic manipulation and we refer readers to recent surveys Bicchi and Kumar [31], Bohg et al. [32] for a comprehensive review.”
Dual arm manipulation - A survey (2012, methodology): “This type of modeling clearly describes the anthropomorphic multi-fingered hands [155] which enables " dexterous manipulation " [156] as illustrated in Bicchi's and Kumar's theoretical survey for robotic grasping [157].”
CASSL: Curriculum Accelerated Self-Supervised Learning (2017, result): “Refer to [19], [20] for surveys of prior work.”
Efficient grasping from RGBD images: Learning using a new rectangle representation (2011, background): “We refer the reader to [9] [10] [11] for a more general survey of past work in robotic grasping.”
Learning Dexterous Grasping with Object-Centric Visual Affordances (2020, neutral): “to determine an optimal grasp [5], [8].”
One-shot learning and generation of dexterous grasps for novel objects (2016, background): “Traditional grasp planning relies on force analysis, which computes the behavior of an object subject to a grip via the laws of classical mechanics (Bicchi and Kumar, 2000).”
Learning to place new objects in a scene (2012, background): “In this section, we describe our robot platforms and details of the system.”