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With the advent of large language models and large-scale robotic datasets, there has been tremendous progress in high-level decision-making for object manipulation.
V-net: Fully convolutional neural networks for volumetric medical image segmentation
F. Milletari, N. Navab, and S.-A. Ahmadi · 2016
Earlier work this paper cites.
Learning to push by grasping: Using multiple tasks for effective learning
L. Pinto and A. Gupta · 2017
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
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Decoupled weight decay regularization
I. Loshchilov and F. Hutter · 2017
Earlier work this paper cites.
6-dof graspnet: Variational grasp generation for object manipulation
A. Mousavian, C. Eppner, and D. Fox · 2019
Earlier work this paper cites.
Rlbench: The robot learning benchmark & learning environment
S. James, Z. Ma, D. R. Arrojo, and A. J. Davison · 2020
Earlier work this paper cites.
6-dof grasping for target-driven object manipulation in clutter
A. Murali, A. Mousavian, C. Eppner, C. Paxton, and D. Fox · 2020
Earlier work this paper cites.
Grasping in the wild: Learning 6dof closed-loop grasping from low-cost demonstrations
S. Song, A. Zeng, J. Lee, and T. Funkhouser · 2020
Earlier work this paper cites.
Graspnet-1billion: A large-scale benchmark for general object grasping
H.-S. Fang, C. Wang, M. Gou, and C. Lu · 2020
Earlier work this paper cites.
Volumetric grasping network: Real-time 6 dof grasp detection in clutter
M. Breyer, J. J. Chung, L. Ott, S. Roland, and N. Juan · 2020
Earlier work this paper cites.
Learning task-oriented grasping for tool manipulation from simulated self-supervision
K. Fang, Y. Zhu, A. Garg, A. Kurenkov, V. Mehta, L. Fei-Fei, and S. Savarese · 2020
Cited alongside, same era.
Contact-graspnet: Efficient 6-dof grasp generation in cluttered scenes
M. Sundermeyer, A. Mousavian, R. Triebel, and D. Fox · 2021
Cited alongside, same era.
Synergies between affordance and geometry: 6-dof grasp detection via implicit representations
Z. Jiang, Y. Zhu, M. Svetlik, K. Fang, and Y. Zhu · 2021
Cited alongside, same era.
Same object, different grasps: Data and semantic knowledge for task-oriented grasping
A. Murali, W. Liu, K. Marino, S. Chernova, and A. Gupta · 2021
Cited alongside, same era.
Object rearrangement using learned implicit collision functions
M. Danielczuk, A. Mousavian, C. Eppner, and D. Fox · 2021
Cited alongside, same era.
Code as policies: Language model programs for embodied control
J. Liang, W. Huang, F. Xia, P. Xu, K. Hausman, B. Ichter, P. Florence, and A. Zeng · 2022
Later among the works it cites.
Progprompt: Generating situated robot task plans using large language models
I. Singh, V. Blukis, A. Mousavian, A. Goyal, D. Xu, J. Tremblay, D. Fox, J. Thomason, and A. Garg · 2022
Later among the works it cites.
Sornet: Spatial object-centric representations for sequential manipulation
W. Yuan, C. Paxton, K. Desingh, and D. Fox · 2022
Later among the works it cites.
Rt-1: Robotics transformer for real-world control at scale
A. Brohan, N. Brown, J. Carbajal, Y. Chebotar, J. Dabis, C. Finn, K. Gopalakrishnan, K. Hausman, A. Herzog, J. Hsu, et al · 2022
Later among the works it cites.
Structformer: Learning spatial structure for language-guided semantic rearrangement of novel objects
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B. Cheng, A. Choudhuri, I. Misra, A. Kirillov, R. Girdhar, and A. G. Schwing · 2021
Cited alongside, same era.
Acronym: A large-scale grasp dataset based on simulation
C. Eppner, A. Mousavian, and D. Fox · 2021
Cited alongside, same era.
Isaac gym: High performance gpu-based physics simulation for robot learning
V. Makoviychuk, L. Wawrzyniak, Y. Guo, M. Lu, K. Storey, M. Macklin, D. Hoeller, N. Rudin, A. Allshire, A. Handa, et al · 2021
Cited alongside, same era.
Learning transferable visual models from natural language supervision
A. Radford, J. W. Kim, C. Hallacy, A. Ramesh, G. Goh, S. Agarwal, G. Sastry, A. Askell, P. Mishkin, J. Clark, et al · 2021
Cited alongside, same era.
Vima: General robot manipulation with multimodal prompts
Y. Jiang, A. Gupta, Z. Zhang, G. Wang, Y. Dou, Y. Chen, L. Fei-Fei, A. Anandkumar, Y. Zhu, and L. Fan · 2022
Cited alongside, same era.
W. Liu, C. Paxton, T. Hermans, and D. Fox · 2022
Later among the works it cites.
Do as i can, not as i say: Grounding language in robotic affordances
A. Brohan, Y. Chebotar, C. Finn, K. Hausman, A. Herzog, D. Ho, J. Ibarz, A. Irpan, E. Jang, R. Julian, et al · 2023
Closest in time.
A. Murali, A. Mousavian, C. Eppner, A. Fishman, and D. Fox · 2023
Closest in time.
Perceiver-actor: A multi-task transformer for robotic manipulation
M. Shridhar, L. Manuelli, and D. Fox · 2023
Closest in time.
Ar2-d2: Training a robot without a robot
J. Duan, Y. R. Wang, M. Shridhar, D. Fox, and R. Krishna · 2023
Closest in time.
Motion policy networks
A. Fishman, A. Murali, C. Eppner, B. Peele, B. Boots, and D. Fox · 2023
Closest in time.