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Representing robotic manipulation tasks as constraints that associate the robot and the environment is a promising way to encode desired robot behaviors.
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M. Toussaint and M. Lopes · 2017
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Attention is all you need
A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, Ł. Kaiser, and I. Polosukhin · 2017
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An incremental constraint-based framework for task and motion planning
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Graph networks as learnable physics engines for inference and control
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Grasp2vec: Learning object representations from self-supervised grasping
E. Jang, C. Devin, V. Vanhoucke, and S. Levine · 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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Learning particle dynamics for manipulating rigid bodies, deformable objects, and fluids
Y. Li, J. Wu, R. Tedrake, J. B. Tenenbaum, and A. Torralba · 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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Sampling-based methods for motion planning with constraints
Z. Kingston, M. Moll, and L. E. Kavraki · 2018
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N. D. Ratliff, J. Issac, D. Kappler, S. Birchfield, and D. Fox · 2018
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Y. Hou, Z. Jia, and M. T. Mason · 2018
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M. A. Toussaint, K. R. Allen, K. A. Smith, and J. B. Tenenbaum · 2018
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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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Densephysnet: Learning dense physical object representations via multi-step dynamic interactions
Z. Xu, J. Wu, A. Zeng, J. B. Tenenbaum, and S. Song · 2019
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J. Mao, C. Gan, P. Kohli, J. B. Tenenbaum, and J. Wu · 2019
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Monet: Unsupervised scene decomposition and representation
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Unsupervised learning of object keypoints for perception and control
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T. Migimatsu and J. Bohg · 2020
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Learning-based model predictive control: Toward safe learning in control
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Object-centric learning with slot attention
F. Locatello, D. Weissenborn, T. Unterthiner, A. Mahendran, G. Heigold, J. Uszkoreit, A. Dosovitskiy, and T. Kipf · 2020
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Keto: Learning keypoint representations for tool manipulation
Z. Qin, K. Fang, Y. Zhu, L. Fei-Fei, and S. Savarese · 2020
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Learning rope manipulation policies using dense object descriptors trained on synthetic depth data
P. Sundaresan, J. Grannen, B. Thananjeyan, A. Balakrishna, M. Laskey, K. Stone, J. E. Gonzalez, and K. Goldberg · 2020
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Keypoints into the future: Self-supervised correspondence in model-based reinforcement learning
L. Manuelli, Y. Li, P. Florence, and R. Tedrake · 2020
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A probabilistic framework for constrained manipulations and task and motion planning under uncertainty
J.-S. Ha, D. Driess, and M. Toussaint · 2020
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Deep visual heuristics: Learning feasibility of mixed-integer programs for manipulation planning
D. Driess, O. Oguz, J.-S. Ha, and M. Toussaint · 2020
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An image is worth 16x16 words: Transformers for image recognition at scale
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Integrated task and motion planning
C. R. Garrett, R. Chitnis, R. Holladay, B. Kim, T. Silver, L. P. Kaelbling, and T. Lozano-Pérez · 2021
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Unsupervised learning of visual 3d keypoints for control
B. Chen, P. Abbeel, and D. Pathak · 2021
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Learning symbolic operators for task and motion planning
T. Silver, R. Chitnis, J. Tenenbaum, L. P. Kaelbling, and T. Lozano-Pérez · 2021
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Look before you leap: Unveiling the power of gpt-4v in robotic vision-language planning
Y. Hu, F. Lin, T. Zhang, L. Yi, and Y. Gao · 2023
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Y. Du, M. Yang, P. Florence, F. Xia, A. Wahid, B. Ichter, P. Sermanet, T. Yu, P. Abbeel, J. B. Tenenbaum, et al · 2023
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3d-llm: Injecting the 3d world into large language models
Y. Hong, H. Zhen, P. Chen, S. Zheng, Y. Du, Z. Chen, and C. Gan · 2023
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Voxposer: Composable 3d value maps for robotic manipulation with language models
W. Huang, C. Wang, R. Zhang, Y. Li, J. Wu, and L. Fei-Fei · 2023
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Rt-2: Vision-language-action models transfer web knowledge to robotic control
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Learning equality constraints for motion planning on manifolds
G. Sutanto, I. R. Fernández, P. Englert, R. K. Ramachandran, and G. Sukhatme · 2021
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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
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Zero-shot text-to-image generation
A. Ramesh, M. Pavlov, G. Goh, S. Gray, C. Voss, A. Radford, M. Chen, and I. Sutskever · 2021
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Emerging properties in self-supervised vision transformers
M. Caron, H. Touvron, I. Misra, H. Jégou, J. Mairal, P. Bojanowski, and A. Joulin · 2021
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Deep vit features as dense visual descriptors
S. Amir, Y. Gandelsman, S. Bagon, and T. Dekel · 2021
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Learning models as functionals of signed-distance fields for manipulation planning
D. Driess, J.-S. Ha, M. Toussaint, and R. Tedrake · 2022
Cited alongside, same era.
Neural descriptor fields: Se (3)-equivariant object representations for manipulation
A. Simeonov, Y. Du, A. Tagliasacchi, J. B. Tenenbaum, A. Rodriguez, P. Agrawal, and V. Sitzmann · 2022
Cited alongside, same era.
A. Brohan, N. Brown, J. Carbajal, Y. Chebotar, X. Chen, K. Choromanski, T. Ding, D. Driess, A. Dubey, C. Finn, et al · 2023
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Physically grounded vision-language models for robotic manipulation
J. Gao, B. Sarkar, F. Xia, T. Xiao, J. Wu, B. Ichter, A. Majumdar, and D. Sadigh · 2023
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Ns3d: Neuro-symbolic grounding of 3d objects and relations
J. Hsu, J. Mao, and J. Wu · 2023
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When and why vision-language models behave like bags-of-words, and what to do about it?
M. Yuksekgonul, F. Bianchi, P. Kalluri, D. Jurafsky, and J. Zou · 2023
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D 3 fields: Dynamic 3d descriptor fields for zero-shot generalizable robotic manipulation
Y. Wang, Z. Li, M. Zhang, K. Driggs-Campbell, J. Wu, L. Fei-Fei, and Y. Li · 2023
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Spawnnet: Learning generalizable visuomotor skills from pre-trained networks
X. Lin, J. So, S. Mahalingam, F. Liu, and P. Abbeel · 2023
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Underactuated Robotics
R. Tedrake · 2023
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Anygrasp: Robust and efficient grasp perception in spatial and temporal domains
H.-S. Fang, C. Wang, H. Fang, M. Gou, J. Liu, H. Yan, W. Liu, Y. Xie, and C. Lu · 2023
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A. Kirillov, E. Mintun, N. Ravi, H. Mao, C. Rolland, L. Gustafson, T. Xiao, S. Whitehead, A. C. Berg, W.-Y. Lo, et al · 2023
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Putting the object back into video object segmentation
H. K. Cheng, S. W. Oh, B. Price, J.-Y. Lee, and A. Schwing · 2023
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Vision transformers need registers
T. Darcet, M. Oquab, J. Mairal, and P. Bojanowski · 2023
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Set-of-mark prompting unleashes extraordinary visual grounding in gpt-4v
J. Yang, H. Zhang, F. Li, X. Zou, C. Li, and J. Gao · 2023
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Tracking everything everywhere all at once
Q. Wang, Y.-Y. Chang, R. Cai, Z. Li, B. Hariharan, A. Holynski, and N. Snavely · 2023
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Pointodyssey: A large-scale synthetic dataset for long-term point tracking
Y. Zheng, A. W. Harley, B. Shen, G. Wetzstein, and L. J. Guibas · 2023
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Cotracker: It is better to track together
N. Karaev, I. Rocco, B. Graham, N. Neverova, A. Vedaldi, and C. Rupprecht · 2023
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Tapir: Tracking any point with per-frame initialization and temporal refinement
C. Doersch, Y. Yang, M. Vecerik, D. Gokay, A. Gupta, Y. Aytar, J. Carreira, and A. Zisserman · 2023
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Dynamic 3d gaussians: Tracking by persistent dynamic view synthesis
J. Luiten, G. Kopanas, B. Leibe, and D. Ramanan · 2023
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nvblox: Gpu-accelerated incremental signed distance field mapping
A. Millane, H. Oleynikova, E. Wirbel, R. Steiner, V. Ramasamy, D. Tingdahl, and R. Siegwart · 2023
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Kinematic-aware prompting for generalizable articulated object manipulation with llms
W. Xia, D. Wang, X. Pang, Z. Wang, B. Zhao, and D. Hu · 2023
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Behavior-1k: A benchmark for embodied ai with 1,000 everyday activities and realistic simulation
C. Li, R. Zhang, J. Wong, C. Gokmen, S. Srivastava, R. Martín-Martín, C. Wang, G. Levine, M. Lingelbach, J. Sun, et al · 2023
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Rvt: Robotic view transformer for 3d object manipulation
A. Goyal, J. Xu, Y. Guo, V. Blukis, Y.-W. Chao, and D. Fox · 2023
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What’s left? concept grounding with logic-enhanced foundation models
J. Hsu, J. Mao, J. Tenenbaum, and J. Wu · 2024
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Physically embodied gaussian splatting: A realtime correctable world model for robotics
J. Abou-Chakra, K. Rana, F. Dayoub, and N. Sünderhauf · 2024
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Doughnet: A visual predictive model for topological manipulation of deformable objects
D. Bauer, Z. Xu, and S. Song · 2024
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Track2act: Predicting point tracks from internet videos enables diverse zero-shot robot manipulation, 2024
H. Bharadhwaj, R. Mottaghi, A. Gupta, and S. Tulsiani · 2024
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Shortest paths in graphs of convex sets
T. Marcucci, J. Umenberger, P. Parrilo, and R. Tedrake · 2024
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Model-based control with sparse neural dynamics
Z. Liu, G. Zhou, J. He, T. Marcucci, F.-F. Li, J. Wu, and Y. Li · 2024
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Dynamic on-palm manipulation via controlled sliding
W. Yang and M. Posa · 2024
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Towards tight convex relaxations for contact-rich manipulation
B. P. Graesdal, S. Y. Chia, T. Marcucci, S. Morozov, A. Amice, P. A. Parrilo, and R. Tedrake · 2024
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Coast: Constraints and streams for task and motion planning
B. Vu, T. Migimatsu, and J. Bohg · 2024
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Real-world robot applications of foundation models: A review
K. Kawaharazuka, T. Matsushima, A. Gambardella, J. Guo, C. Paxton, and A. Zeng · 2024
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Copa: General robotic manipulation through spatial constraints of parts with foundation models
H. Huang, F. Lin, Y. Hu, S. Wang, and Y. Gao · 2024
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Moka: Open-vocabulary robotic manipulation through mark-based visual prompting
F. Liu, K. Fang, P. Abbeel, and S. Levine · 2024
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Pivot: Iterative visual prompting elicits actionable knowledge for vlms
S. Nasiriany, F. Xia, W. Yu, T. Xiao, J. Liang, I. Dasgupta, A. Xie, D. Driess, A. Wahid, Z. Xu, et al · 2024
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Spatialvlm: Endowing vision-language models with spatial reasoning capabilities
B. Chen, Z. Xu, S. Kirmani, B. Ichter, D. Sadigh, L. Guibas, and F. Xia · 2024
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Grounding language plans in demonstrations through counterfactual perturbations
Y. Wang, T.-H. Wang, J. Mao, M. Hagenow, and J. Shah · 2024
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Physically grounded vision-language models for robotic manipulation
J. Gao, B. Sarkar, F. Xia, T. Xiao, J. Wu, B. Ichter, A. Majumdar, and D. Sadigh · 2024
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Robopoint: A vision-language model for spatial affordance prediction for robotics
W. Yuan, J. Duan, V. Blukis, W. Pumacay, R. Krishna, A. Murali, A. Mousavian, and D. Fox · 2024
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Manipulate-anything: Automating real-world robots using vision-language models
J. Duan, W. Yuan, W. Pumacay, Y. R. Wang, K. Ehsani, D. Fox, and R. Krishna · 2024
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Eyes wide shut? exploring the visual shortcomings of multimodal llms
S. Tong, Z. Liu, Y. Zhai, Y. Ma, Y. LeCun, and S. Xie · 2024
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Sugarcrepe: Fixing hackable benchmarks for vision-language compositionality
C.-Y. Hsieh, J. Zhang, Z. Ma, A. Kembhavi, and R. Krishna · 2024
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Affordance-guided reinforcement learning via visual prompting
O. Y. Lee, A. Xie, K. Fang, K. Pertsch, and C. Finn · 2024
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Bootstap: Bootstrapped training for tracking-any-point
C. Doersch, Y. Yang, D. Gokay, P. Luc, S. Koppula, A. Gupta, J. Heyward, R. Goroshin, J. Carreira, and A. Zisserman · 2024
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Spatialtracker: Tracking any 2d pixels in 3d space
Y. Xiao, Q. Wang, S. Zhang, N. Xue, S. Peng, Y. Shen, and X. Zhou · 2024
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Manipllm: Embodied multimodal large language model for object-centric robotic manipulation
X. Li, M. Zhang, Y. Geng, H. Geng, Y. Long, Y. Shen, R. Zhang, J. Liu, and H. Dong · 2024
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A3vlm: Actionable articulation-aware vision language model
S. Huang, H. Chang, Y. Liu, Y. Zhu, H. Dong, P. Gao, A. Boularias, and H. Li · 2024
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Rvt-2: Learning precise manipulation from few demonstrations
A. Goyal, V. Blukis, J. Xu, Y. Guo, Y.-W. Chao, and D. Fox · 2024
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