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Industrial insertion tasks are often performed repetitively with parts that are subject to tight tolerances and prone to breakage.
“Automatic synthesis of fine-motion strategies for robots”
Tomas Lozano-Perez, Matthew Mason and Russell Taylor · 1984
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“Automatic Assembly by G. Boothroyd, C. Poli and LE Murch, Marcel Dekker, New York, 378 pp., 1982”
KE McKee · 1985
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“Motion planning and the design of orienting devices for vibratory part feeders”
Tomas Lozano-P“’erez · 1986
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“A correct and complete algorithm for the generation of mechanical assembly sequences”
LS De and Arthur Sanderson · 1989
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“Some paradigms for the automated design of parts feeders”
Balas Natarajan · 1989
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“Orienting polygonal parts without sensors”
Kenneth Goldberg · 1993
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“Fine motion strategies for robotic peg-hole insertion”
Hong Qiao, BS Dalay and RM Parkin · 1995
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“Localization and manipulation of small parts using GelSight tactile sensing”
Rui Li et al · 2014
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“Adam: A method for stochastic optimization”
Diederik Kingma and Jimmy Ba · 2015
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“Gelsight: Highresolution robot tactile sensors for estimating geometry and force”
Wenzhen Yuan, Siyuan Dong and Edward Adelson · 2017
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“Addressing function approximation error in actor-critic methods”
Scott Fujimoto, Herke Hoof and David Meger · 2018
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“Ray: A distributed framework for emerging AI applications”
Philipp Moritz et al · 2018
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“A Learning Framework for High Precision Industrial Assembly”
Yongxiang Fan, Jieliang Luo and Masayoshi Tomizuka · 2019
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“Residual reinforcement learning for robot control”
Tobias Johannink et al · 2019
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“Benchmarking protocols for evaluating small parts robotic assembly systems”
K. Kimble et al · 2020
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“Digit: A novel design for a low-cost compact high-resolution tactile sensor with application to in-hand manipulation”
Mike Lambeta et al · 2020
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“Designing network design spaces”
Ilija Radosavovic et al · 2020
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“Deep reinforcement learning for industrial insertion tasks with visual inputs and natural rewards”
Gerrit Schoettler et al · 2020
“Elastic tactile simulation towards tactile-visual perception”
Yikai Wang et al · 2021
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“Using Collocated Vision and Tactile Sensors for Visual Servoing and Localization”
Arkadeep Chaudhury, Timothy Man, Wenzhen Yuan and Christopher Atkeson · 2022
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“Visuotactile-RL: Learning Multimodal Manipulation Policies with Deep Reinforcement Learning”
Johanna Hansen et al · 2022
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“Contact-rich manipulation of a flexible object based on deep predictive learning using vision and tactility”
Hideyuki Ichiwara et al · 2022
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“Tactile Pose Estimation and Policy Learning for Unknown Object Manipulation”
Tarik Kelestemur, Robert Platt and Taskin Padir · 2022
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“Meta-reinforcement learning for robotic industrial insertion tasks”
Gerrit Schoettler et al · 2020
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Lars Berscheid and Torsten Kr“”oger · 2021
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Pete Florence et al · 2021
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“Robust multi-modal policies for industrial assembly via reinforcement learning and demonstrations: A large-scale study”
Jianlan Luo et al · 2021
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“InsertionNet - A Scalable Solution for Insertion”
Oren Spector and Dotan Castro · 2021
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Yotto Koga, Heather Kerrick and Sachin Chitta · 2022
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Yashraj Narang et al · 2022
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Ryo Okumura, Nobuki Nishio and Tadahiro Taniguchi · 2022
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“InsertionNet 2.0: Minimal Contact Multi-Step Insertion Using Multimodal Multiview Sensory Input”
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“TACTO: A Fast, Flexible, and Open-source Simulator for High-resolution Vision-based Tactile Sensors”
Shaoxiong Wang, Mike Lambeta, Po-Wei Chou and Roberto Calandra · 2022
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“You Only Demonstrate Once: Category-Level Manipulation from Single Visual Demonstration”
Bowen Wen, Wenzhao Lian, Kostas. Bekris and Stefan Schaal · 2022
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“Offline meta-reinforcement learning for industrial insertion”
Tony Zhao et al · 2022
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