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We introduce VIOLA, an object-centric imitation learning approach to learning closed-loop visuomotor policies for robot manipulation.
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Probabilistic movement primitives
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Deep residual learning for image recognition
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Deep spatial autoencoders for visuomotor learning
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Building machines that learn and think like people
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Mask r-cnn
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Attention is all you need
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Neural discrete representation learning
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Deep imitation learning for complex manipulation tasks from virtual reality teleoperation
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Cascade r-cnn: Delving into high quality object detection
Z. Cai and N. Vasconcelos · 2018
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Using probabilistic movement primitives in robotics
A. Paraschos, C. Daniel, J. Peters, and G. Neumann · 2018
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Deep object-centric representations for generalizable robot learning
C. Devin, P. Abbeel, T. Darrell, and S. Levine · 2018
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Dense object nets: Learning dense visual object descriptors by and for robotic manipulation
P. R. Florence, L. Manuelli, and R. Tedrake · 2018
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Deep object pose estimation for semantic robotic grasping of household objects
J. Tremblay, T. To, B. Sundaralingam, Y. Xiang, D. Fox, and S. Birchfield · 2018
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Bert: Pre-training of deep bidirectional transformers for language understanding
J. Devlin, M.-W. Chang, K. Lee, and K. Toutanova · 2018
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Vision-based multi-task manipulation for inexpensive robots using end-to-end learning from demonstration
R. Rahmatizadeh, P. Abolghasemi, L. Bölöni, and S. Levine · 2018
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Causal confusion in imitation learning
P. De Haan, D. Jayaraman, and S. Levine · 2019
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Exploring the limitations of behavior cloning for autonomous driving
F. Codevilla, E. Santana, A. M. López, and A. Gaidon · 2019
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Imitation learning via off-policy distribution matching
I. Kostrikov, O. Nachum, and J. Tompson · 2019
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Uniter: Universal image-text representation learning
Y.-C. Chen, L. Li, L. Yu, A. El Kholy, F. Ahmed, Z. Gan, Y. Cheng, and J. Liu · 2020
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Image augmentation is all you need: Regularizing deep reinforcement learning from pixels
I. Kostrikov, D. Yarats, and R. Fergus · 2020
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Critic regularized regression
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Random erasing data augmentation
Z. Zhong, L. Zheng, G. Kang, S. Li, and Y. Yang · 2020
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robosuite: A modular simulation framework and benchmark for robot learning
Y. Zhu, J. Wong, A. Mandlekar, and R. Martín-Martín · 2020
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Self-supervised correspondence in visuomotor policy learning
P. Florence, L. Manuelli, and R. Tedrake · 2019
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X. Zhou, D. Wang, and P. Krähenbühl · 2019
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Deep object-centric policies for autonomous driving
D. Wang, C. Devin, Q.-Z. Cai, F. Yu, and T. Darrell · 2019
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kpam: Keypoint affordances for category-level robotic manipulation
L. Manuelli, W. Gao, P. Florence, and R. Tedrake · 2019
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Unsupervised learning of object keypoints for perception and control
T. D. Kulkarni, A. Gupta, C. Ionescu, S. Borgeaud, M. Reynolds, A. Zisserman, and V. Mnih · 2019
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Monet: Unsupervised scene decomposition and representation
C. P. Burgess, L. Matthey, N. Watters, R. Kabra, I. Higgins, M. Botvinick, and A. Lerchner · 2019
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Vl-bert: Pre-training of generic visual-linguistic representations
W. Su, X. Zhu, Y. Cao, B. Li, L. Lu, F. Wei, and J. Dai · 2019
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The magical benchmark for robust imitation
S. Toyer, R. Shah, A. Critch, and S. Russell · 2020
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Learning to play by imitating humans
R. Dinyari, P. Sermanet, and C. Lynch · 2020
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An image is worth 16x16 words: Transformers for image recognition at scale
A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, et al · 2020
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Object-aware regularization for addressing causal confusion in imitation learning
J. Park, Y. Seo, C. Liu, L. Zhao, T. Qin, J. Shin, and T.-Y. Liu · 2021
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What matters in learning from offline human demonstrations for robot manipulation
A. Mandlekar, D. Xu, J. Wong, S. Nasiriany, C. Wang, R. Kulkarni, L. Fei-Fei, S. Savarese, Y. Zhu, and R. Martín-Martín · 2021
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Learning to rearrange deformable cables, fabrics, and bags with goal-conditioned transporter networks
D. Seita, P. Florence, J. Tompson, E. Coumans, V. Sindhwani, K. Goldberg, and A. Zeng · 2021
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Volumetric grasping network: Real-time 6 dof grasp detection in clutter
M. Breyer, J. J. Chung, L. Ott, R. Siegwart, and J. Nieto · 2021
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Synergies between affordance and geometry: 6-dof grasp detection via implicit representations
Z. Jiang, Y. Zhu, M. Svetlik, K. Fang, and Y. Zhu · 2021
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Generalization through hand-eye coordination: An action space for learning spatially-invariant visuomotor control
C. Wang, R. Wang, A. Mandlekar, L. Fei-Fei, S. Savarese, and D. Xu · 2021
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Bottom-up skill discovery from unsegmented demonstrations for long-horizon robot manipulation
Y. Zhu, P. Stone, and Y. Zhu · 2022
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Detecting twenty-thousand classes using image-level supervision
X. Zhou, R. Girdhar, A. Joulin, P. Krähenbühl, and I. Misra · 2022
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Cliport: What and where pathways for robotic manipulation
M. Shridhar, L. Manuelli, and D. Fox · 2022
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S. Tyree, J. Tremblay, T. To, J. Cheng, T. Mosier, J. Smith, and S. Birchfield · 2022
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Vision-based manipulators need to also see from their hands
K. Hsu, M. J. Kim, R. Rafailov, J. Wu, and C. Finn · 2022
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Eyes on the prize: Improved perception for robust dynamic grasping
B. Burgess-Limerick, C. Lehnert, J. Leitner, and P. Corke · 2022
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Look closer: Bridging egocentric and third-person views with transformers for robotic manipulation
R. Jangir, N. Hansen, S. Ghosal, M. Jain, and X. Wang · 2022
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