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Manipulation planning is the task of computing robot trajectories that move a set of objects to their target configuration while satisfying physically feasibility.
Rapidly-exploring random trees: A new tool for path planning
S. M. LaValle · 1998
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Automated construction of robotic manipulation programs
R. Diankov · 2010
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Task planning with continuous actions and nondeterministic motion planning queries
K. Hauser · 2010
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Hierarchical planning in the now
L. P. Kaelbling and T. Lozano-Pérez · 2010
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Point set registration: Coherent point drift
A. Myronenko and X. Song · 2010
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Sampling-based algorithms for optimal motion planning
S. Karaman and E. Frazzoli · 2011
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Perception, planning, and execution for mobile manipulation in unstructured environments
S. Chitta, E. G. Jones, M. Ciocarlie, and K. Hsiao · 2012
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Combined task and motion planning through an extensible planner-independent interface layer
S. Srivastava, E. Fang, L. Riano, R. Chitnis, S. Russell, and P. Abbeel · 2014
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High precision grasp pose detection in dense clutter
M. Gualtieri, A. Ten Pas, K. Saenko, and R. Platt · 2016
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Dex-net 1.0: A cloud-based network of 3d objects for robust grasp planning using a multi-armed bandit model with correlated rewards
J. Mahler, F. T. Pokorny, B. Hou, M. Roderick, M. Laskey, M. Aubry, K. Kohlhoff, T. Kröger, J. Kuffner, and K. Goldberg · 2016
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Drake: A planning, control, and analysis toolbox for nonlinear dynamical systems, 2016
R. Tedrake and the Drake Development Team · 2016
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Labelfusion: A pipeline for generating ground truth labels for real rgbd data of cluttered scenes
P. Marion, P. R. Florence, L. Manuelli, and R. Tedrake · 2017
Cited alongside, same era.
Shape completion enabled robotic grasping
J. Varley, C. DeChant, A. Richardson, J. Ruales, and P. Allen · 2017
Cited alongside, same era.
Multi-view self-supervised deep learning for 6d pose estimation in the amazon picking challenge
A. Zeng, K.-T. Yu, S. Song, D. Suo, E. Walker, A. Rodriguez, and J. Xiao · 2017
Cited alongside, same era.
W. Gao and R. Tedrake · 2018
Cited alongside, same era.
Sampling-based methods for factored task and motion planning
C. R. Garrett, T. Lozano-Pérez, and L. P. Kaelbling · 2018
Cited alongside, same era.
Robotic pick-and-place of novel objects in clutter with multi-affordance grasping and cross-domain image matching
A. Zeng, S. Song, K.-T. Yu, E. Donlon, F. R. Hogan, M. Bauza, D. Ma, O. Taylor, M. Liu, E. Romo, et al · 2018
Later among the works it cites.
Learning to reconstruct shapes from unseen classes
X. Zhang, Z. Zhang, C. Zhang, J. Tenenbaum, B. Freeman, and J. Wu · 2018
Later among the works it cites.
Object placement planning and optimization for robot manipulators
J. A. Haustein, K. Hang, J. Stork, and D. Kragic · 2019
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Robust grasp planning over uncertain shape completions
J. Lundell, F. Verdoja, and V. Kyrki · 2019
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Learning ambidextrous robot grasping policies
J. Mahler, M. Matl, V. Satish, M. Danielczuk, B. DeRose, S. McKinley, and K. Goldberg · 2019
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Pick and place without geometric object models
M. Gualtieri, A. ten Pas, and R. Platt · 2018
Cited alongside, same era.
Qt-opt: Scalable deep reinforcement learning for vision-based robotic manipulation
D. Kalashnikov, A. Irpan, P. Pastor, J. Ibarz, A. Herzog, E. Jang, D. Quillen, E. Holly, M. Kalakrishnan, V. Vanhoucke, et al · 2018
Cited alongside, same era.
Real-time perception meets reactive motion generation
D. Kappler, F. Meier, J. Issac, J. Mainprice, C. G. Cifuentes, M. Wüthrich, V. Berenz, S. Schaal, N. Ratliff, and J. Bohg · 2018
Cited alongside, same era.
Closing the loop for robotic grasping: A real-time, generative grasp synthesis approach
D. Morrison, P. Corke, and J. Leitner · 2018
Cited alongside, same era.
Category-level 6d object pose recovery in depth images
C. Sahin and T.-K. Kim · 2018
Cited alongside, same era.
Multi-modal geometric learning for grasping and manipulation
D. Watkins-Valls, J. Varley, and P. Allen · 2018
Cited alongside, same era.
Finding locally optimal, collision-free trajectories with sequential convex optimization
J. Schulman, J. Ho, A. X. Lee, I. Awwal, H. Bradlow, and P. Abbeel
Cited in the paper.
kpam: Keypoint affordances for category-level robotic manipulation
L. Manuelli, W. Gao, P. Florence, and R. Tedrake · 2019
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Occupancy networks: Learning 3d reconstruction in function space
L. Mescheder, M. Oechsle, M. Niemeyer, S. Nowozin, and A. Geiger · 2019
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Deepsdf: Learning continuous signed distance functions for shape representation
J. J. Park, P. Florence, J. Straub, R. Newcombe, and S. Lovegrove · 2019
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Inferring occluded geometry improves performance when retrieving an object from dense clutter
A. Price, L. Jin, and D. Berenson · 2019
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Normalized object coordinate space for category-level 6d object pose and size estimation
H. Wang, S. Sridhar, J. Huang, J. Valentin, S. Song, and L. J. Guibas · 2019
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