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Robots need to manipulate objects in constrained environments like shelves and cabinets when assisting humans in everyday settings like homes and offices.
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M. Sharma, J. Liang, J. Zhao, A. LaGrassa, and O. Kroemer · 2020
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Rearrangement with nonprehensile manipulation using deep reinforcement learning
W. Yuan, J. A. Stork, D. Kragic, M. Y. Wang, and K. Hang · 2018
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Linear push policies to increase grasp access for robot bin picking
M. Danielczuk, J. Mahler, C. Correa, and K. Goldberg · 2018
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Learning synergies between pushing and grasping with self-supervised deep reinforcement learning
A. Zeng, S. Song, S. Welker, J. Lee, A. Rodriguez, and T. Funkhouser · 2018
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From skills to symbols: Learning symbolic representations for abstract high-level planning
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Open3D: A modern library for 3D data processing
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Learning efficient push and grasp policy in a totebox from simulation
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Learning pregrasp manipulation of objects from ungraspable poses
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A modular robotic arm control stack for research: Franka-interface and frankapy
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Contact mode guided sampling-based planning for quasistatic dexterous manipulation in 2d
X. Cheng, E. Huang, Y. Hou, and M. T. Mason · 2021
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Learning compositional models of robot skills for task and motion planning
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Contact-graspnet: Efficient 6-dof grasp generation in cluttered scenes
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Search-based task planning with learned skill effect models for lifelong robotic manipulation
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An efficient closed-form method for optimal hybrid force-velocity control
Y. Hou and M. T. Mason · 2021
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Manipulation of unknown objects via contact configuration regulation
N. Doshi, O. Taylor, and A. Rodriguez · 2022
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Contact mode guided motion planning for quasidynamic dexterous manipulation in 3d
X. Cheng, E. Huang, Y. Hou, and M. T. Mason · 2022
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Learning to grasp the ungraspable with emergent extrinsic dexterity
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