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Task and Motion Planning (TAMP) is essential for robots to interact with the world and accomplish complex tasks.
A kinematic notation for lower-pair mechanisms based on matrices
Jacques Denavit and Richard S Hartenberg · 1955
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Pddl— the planning domain definition language
Constructions Aeronautiques, Adele Howe, Craig Knoblock, ISI Drew McDermott, Ashwin Ram, Manuela Veloso, Daniel Weld, David Wilkins SRI, Anthony Barrett, Dave Christianson, et al · 1998
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On-line stability margin and attitude estimation for dynamic articulating mobile robots
Antonio Diaz-Calderon and Alonzo Kelly · 2005
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A human aware mobile robot motion planner
Emrah Akin Sisbot, Luis F Marin-Urias, Rachid Alami, and Thierry Simeon · 2007
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A comparison between the denavit–hartenberg and the screw-based methods used in kinematic modeling of robot manipulators
CR Rocha, CP Tonetto, and Altamir Dias · 2011
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Neural-symbolic learning and reasoning: contributions and challenges
Garcez Artur d’Avila, Raedt Luc De, Földiak Peter, Hitzler Pascal, Icard Thomas, Kühnberger Kai-Uwe, Miikkulainen Risto, et al · 2015
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Force-based stability margin for multi-legged robots
Mahdi Agheli and Stephen S Nestinger · 2016
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Deep learning has outlived its usefulness as a buzz-phrase, 2018
Y LeCun · 2018
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Differentiable programming for image processing and deep learning in halide
Tzu-Mao Li, Michaël Gharbi, Andrew Adams, Frédo Durand, and Jonathan Ragan-Kelley · 2018
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Tune: A research platform for distributed model selection and training
Richard Liaw, Eric Liang, Robert Nishihara, Philipp Moritz, Joseph E Gonzalez, and Ion Stoica · 2018
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Learning to guide task and motion planning using score-space representation
Beomjoon Kim, Zi Wang, Leslie Pack Kaelbling, and Tomás Lozano-Pérez · 2019
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From system 1 deep learning to system 2 deep learning
Yoshua Bengio et al · 2019
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Vision-based target-following guider for mobile robot
Mingyi Zhang, Xilong Liu, De Xu, Zhiqiang Cao, and Junzhi Yu · 2019
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Differentiable programming tensor networks
Hai-Jun Liao, Jin-Guo Liu, Lei Wang, and Tao Xiang · 2019
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A differentiable programming system to bridge machine learning and scientific computing
Mike Innes, Alan Edelman, Keno Fischer, Chris Rackauckas, Elliot Saba, Viral B Shah, and Will Tebbutt · 2019
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A mobile robotic chemist
Benjamin Burger, Phillip M Maffettone, Vladimir V Gusev, Catherine M Aitchison, Yang Bai, Xiaoyan Wang, Xiaobo Li, Ben M Alston, Buyi Li, Rob Clowes, et al · 2020
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Motion planning networks: Bridging the gap between learning-based and classical motion planners
Ahmed Hussain Qureshi, Yinglong Miao, Anthony Simeonov, and Michael C Yip · 2020
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Pddlstream: Integrating symbolic planners and blackbox samplers via optimistic adaptive planning
Caelan Reed Garrett, Tomás Lozano-Pérez, and Leslie Pack Kaelbling · 2020
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Sequence-based plan feasibility prediction for efficient task and motion planning
Zhutian Yang, Caelan Reed Garrett, and Dieter Fox · 2022
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Autonomous electric vehicle battery disassembly based on neurosymbolic computing
Hengwei Zhang, Hua Yang, Haitao Wang, Zhigang Wang, Shengmin Zhang, and Ming Chen · 2022
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Approaching motion planning for mobile manipulators considering the uncertainty of self-positioning and object’s pose estimation
Kimitoshi Yamazaki, Satoshi Suzuki, and Yusuke Kuribayashi · 2022
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Flexible gait transition for six wheel-legged robot with unstructured terrains
Zhihua Chen, Jiehao Li, Shoukun Wang, Junzheng Wang, and Liling Ma · 2022
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Multiple mobile robot task and motion planning: A survey
Luke Antonyshyn, Jefferson Silveira, Sidney Givigi, and Joshua Marshall · 2023
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A review of the challenges in mobile manipulation: systems design and robocup challenges
Martin Sereinig, Wolfgang Werth, and Lisa-Marie Faller · 2020
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Integrated task and motion planning
Caelan Reed Garrett, Rohan Chitnis, Rachel Holladay, Beomjoon Kim, Tom Silver, Leslie Pack Kaelbling, and Tomás Lozano-Pérez · 2021
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Learning compositional models of robot skills for task and motion planning
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A state-of-the-art review on mobile robotics tasks using artificial intelligence and visual data
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Neurosymbolic task and motion planner for disassembly electric vehicle batteries
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Additive manufacturing using mobile robots: Opportunities and challenges for building construction
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Design and experimental validation of deep reinforcement learning-based fast trajectory planning and control for mobile robot in unknown environment
Runqi Chai, Hanlin Niu, Joaquin Carrasco, Farshad Arvin, Hujun Yin, and Barry Lennox · 2022
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Toward the third generation artificial intelligence
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Neurosymbolic ai: The 3 rd wave
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Team with the strong, work with the wise
Hsiao-Ying Lin · 2023
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A novel knowledge-driven flexible human–robot hybrid disassembly line and its key technologies for electric vehicle batteries
Hengwei Zhang, Yisheng Zhang, Zhigang Wang, Shengmin Zhang, Huaicheng Li, and Ming Chen · 2023
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Development of an autonomous, explainable, robust robotic system for electric vehicle battery disassembly
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Learning to search in task and motion planning with streams
Mohamed Khodeir, Ben Agro, and Florian Shkurti · 2023
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Differentiable programming: Efficient smoothing of control-flow-induced discontinuities
Sebastian Christodoulou and Uwe Naumann · 2023
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