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Manipulating objects without grasping them is an essential component of human dexterity, referred to as non-prehensile manipulation.
Meta-world: A benchmark and evaluation for multi-task and meta reinforcement learning
T. Yu, D. Quillen, Z. He, R. Julian, K. Hausman, C. Finn, and S. Levine · 1910
Earlier work this paper cites.
Progress in nonprehensile manipulation
M. T. Mason · 1999
Earlier work this paper cites.
Discovery of complex behaviors through contact-invariant optimization
I. Mordatch, E. Todorov, and Z. Popović · 2012
Earlier work this paper cites.
Mujoco: A physics engine for model-based control
E. Todorov, T. Erez, and Y. Tassa · 2012
Earlier work this paper cites.
Playing atari with deep reinforcement learning
V. Mnih, K. Kavukcuoglu, D. Silver, A. Graves, I. Antonoglou, D. Wierstra, and M. Riedmiller · 2013
Earlier work this paper cites.
Model based training, detection and pose estimation of texture-less 3d objects in heavily cluttered scenes
S. Hinterstoisser, V. Lepetit, S. Ilic, S. Holzer, G. Bradski, K. Konolige, and N. Navab · 2013
Earlier work this paper cites.
Continuous control with deep reinforcement learning
T. P. Lillicrap, J. J. Hunt, A. Pritzel, N. Heess, T. Erez, Y. Tassa, D. Silver, and D. Wierstra · 2015
Earlier work this paper cites.
Deep reinforcement learning in parameterized action space
M. Hausknecht and P. Stone · 2015
Earlier work this paper cites.
More than a million ways to be pushed. a high-fidelity experimental dataset of planar pushing
K.-T. Yu, M. Bauza, N. Fazeli, and A. Rodriguez · 2016
Earlier work this paper cites.
Pointnet: Deep learning on point sets for 3d classification and segmentation
C. R. Qi, H. Su, K. Mo, and L. J. Guibas · 2016
Earlier work this paper cites.
Pointnet++: Deep hierarchical feature learning on point sets in a metric space
C. R. Qi, L. Yi, H. Su, and L. J. Guibas · 2017
Earlier work this paper cites.
Addressing function approximation error in actor-critic methods
S. Fujimoto, H. Hoof, and D. Meger · 2018
Earlier work this paper cites.
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
Earlier work this paper cites.
Soft actor-critic: Off-policy maximum entropy deep reinforcement learning with a stochastic actor
T. Haarnoja, A. Zhou, P. Abbeel, and S. Levine · 2018
Earlier work this paper cites.
J. Xiong, Q. Wang, Z. Yang, P. Sun, L. Han, Y. Zheng, H. Fu, T. Zhang, J. Liu, and H. Liu · 2018
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Posecnn: A convolutional neural network for 6d object pose estimation in cluttered scenes
Y. Xiang, T. Schmidt, V. Narayanan, and D. Fox · 2018
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Robust execution of contact-rich motion plans by hybrid force-velocity control
Y. Hou and M. T. Mason · 2019
Cited alongside, same era.
Spatial action maps for mobile manipulation
J. Wu, X. Sun, A. Zeng, S. Song, J. Lee, S. Rusinkiewicz, and T. Funkhouser · 2020
Cited alongside, same era.
Transporter networks: Rearranging the visual world for robotic manipulation
A. Zeng, P. Florence, J. Tompson, S. Welker, J. Chien, M. Attarian, T. Armstrong, I. Krasin, D. Duong, V. Sindhwani, and J. Lee · 2020
Cited alongside, same era.
Neural descriptor fields: Se(3)-equivariant object representations for manipulation, 2021
A. Simeonov, Y. Du, A. Tagliasacchi, J. B. Tenenbaum, A. Rodriguez, P. Agrawal, and V. Sitzmann · 2021
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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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Umpnet: Universal manipulation policy network for articulated objects
Z. Xu, H. Zhanpeng, and S. Song · 2022
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Learning preconditions of hybrid force-velocity controllers for contact-rich manipulation
J. Liang, X. Cheng, and O. Kroemer · 2022
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Learning to grasp the ungraspable with emergent extrinsic dexterity
W. Zhou and D. Held · 2022
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“good robot!”: Efficient reinforcement learning for multi-step visual tasks with sim to real transfer
A. Hundt, B. Killeen, N. Greene, H. Wu, H. Kwon, C. Paxton, and G. D. Hager · 2020
Cited alongside, same era.
Continuous-discrete reinforcement learning for hybrid control in robotics
M. Neunert, A. Abdolmaleki, M. Wulfmeier, T. Lampe, T. Springenberg, R. Hafner, F. Romano, J. Buchli, N. Heess, and M. Riedmiller · 2020
Cited alongside, same era.
robosuite: A modular simulation framework and benchmark for robot learning
Y. Zhu, J. Wong, A. Mandlekar, R. Martín-Martín, A. Joshi, S. Nasiriany, and Y. Zhu · 2020
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Where2act: From pixels to actions for articulated 3d objects
K. Mo, L. J. Guibas, M. Mukadam, A. Gupta, and S. Tulsiani · 2021
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Generalization in dexterous manipulation via geometry-aware multi-task learning
W. Huang, I. Mordatch, P. Abbeel, and D. Pathak · 2021
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Goal-auxiliary actor-critic for 6d robotic grasping with point clouds
L. Wang, Y. Xiang, W. Yang, A. Mousavian, and D. Fox · 2021
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A system for general in-hand object re-orientation
T. Chen, J. Xu, and P. Agrawal · 2021
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Dexpoint: Generalizable point cloud reinforcement learning for sim-to-real dexterous manipulation
Y. Qin, B. Huang, Z.-H. Yin, H. Su, and X. Wang · 2022
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Frame mining: a free lunch for learning robotic manipulation from 3d point clouds
M. Liu, X. Li, Z. Ling, Y. Li, and H. Su · 2022
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Visual dexterity: In-hand dexterous manipulation from depth
T. Chen, M. Tippur, S. Wu, V. Kumar, E. Adelson, and P. Agrawal · 2022
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A hybrid approach for learning to shift and grasp with elaborate motion primitives
Z. Feldman, H. Ziesche, N. A. Vien, and D. Di Castro · 2022
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ToolFlowNet: Robotic Manipulation with Tools via Predicting Tool Flow from Point Clouds
D. Seita, Y. Wang, S. Shetty, E. Li, Z. Erickson, and D. Held · 2022
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Perceiver-actor: A multi-task transformer for robotic manipulation
M. Shridhar, L. Manuelli, and D. Fox · 2022
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Structdiffusion: Object-centric diffusion for semantic rearrangement of novel objects
W. Liu, T. Hermans, S. Chernova, and C. Paxton · 2022
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Deep visual constraints: Neural implicit models for manipulation planning from visual input, 2022
J.-S. Ha, D. Driess, and M. Toussaint · 2022
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