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Prompt-based learning has emerged as a successful paradigm in natural language processing, where a single general-purpose language model can be instructed to perform any task specified by input prompts.
Word learning in children: An examination of fast mapping
Heibeck, T. H. and Markman, E. M · 1987
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Real-time tracking and pose estimation for industrial objects using geometric features
Yoon, Y., DeSouza, G., and Kak, A · 2003
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Language-Conditioned Imitation Learning for Robot Manipulation Tasks, October 2020
Stepputtis, S., Campbell, J., Phielipp, M., Lee, S., Baral, C., and Amor, H. B · 2010
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Faster r-cnn: Towards real-time object detection with region proposal networks
Ren, S., He, K., Girshick, R., and Sun, J · 2015
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Ubernet: Training a ‘universal’ convolutional neural network for low-, mid-, and high-level vision using diverse datasets and limited memory
Kokkinos, I · 2016
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One-shot imitation learning
Duan, Y., Andrychowicz, M., Stadie, B. C., Ho, J., Schneider, J., Sutskever, I., Abbeel, P., and Zaremba, W · 2017
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One-shot visual imitation learning via meta-learning
Finn, C., Yu, T., Zhang, T., Abbeel, P., and Levine, S · 2017
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Mask r-cnn
He, K., Gkioxari, G., Dollár, P., and Girshick, R · 2017
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Grounded language learning in a simulated 3d world
Hermann, K. M., Hill, F., Green, S., Wang, F., Faulkner, R., Soyer, H., Szepesvari, D., Czarnecki, W. M., Jaderberg, M., Teplyashin, D., Wainwright, M., Apps, C., Hassabis, D., and Blunsom, P · 2017
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SGDR: stochastic gradient descent with warm restarts
Loshchilov, I. and Hutter, F · 2017
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World of bits: an open-domain platform for web-based agents
Shi, T. T., Karpathy, A., Fan, L. J., Hernandez, J., and Liang, P · 2017
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Attention is all you need
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L., and Polosukhin, I · 2017
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Surreal: Open-source reinforcement learning framework and robot manipulation benchmark
Fan, L., Zhu, Y., Zhu, J., Liu, Z., Zeng, O., Gupta, A., Creus-Costa, J., Savarese, S., and Fei-Fei, L · 2018
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The natural language decathlon: Multitask learning as question answering
McCann, B., Keskar, N. S., Xiong, C., and Socher, R · 2018
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One-shot high-fidelity imitation: Training large-scale deep nets with rl
Paine, T. L., Colmenarejo, S. G., Wang, Z., Reed, S., Aytar, Y., Pfaff, T., Hoffman, M. W., Barth-Maron, G., Cabi, S., Budden, D., and de Freitas, N · 2018
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Virtualhome: Simulating household activities via programs
Puig, X., Ra, K., Boben, M., Li, J., Wang, T., Fidler, S., and Torralba, A · 2018
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Improving language understanding by generative pre-training
Radford, A., Narasimhan, K., Salimans, T., and Sutskever, I · 2018
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Mid-level visual representations improve generalization and sample efficiency for learning visuomotor policies
Sax, A., Emi, B., Zamir, A. R., Guibas, L., Savarese, S., and Malik, J · 2018
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Robonet: Large-scale multi-robot learning
Dasari, S., Ebert, F., Tian, S., Nair, S., Bucher, B., Schmeckpeper, K., Singh, S., Levine, S., and Finn, C · 2019
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Surreal-system: Fully-integrated stack for distributed deep reinforcement learning
Fan, L., Zhu, Y., Zhu, J., Liu, Z., Zeng, O., Gupta, A., Creus-Costa, J., Savarese, S., and Fei-Fei, L · 2019
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Self-supervised correspondence in visuomotor policy learning
Florence, P., Manuelli, L., and Tedrake, R · 2019
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Relay policy learning: Solving long-horizon tasks via imitation and reinforcement learning
Gupta, A., Kumar, V., Lynch, C., Levine, S., and Hausman, K · 2019
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Continuous relaxation of symbolic planner for one-shot imitation learning
Huang, D., Xu, D., Zhu, Y., Garg, A., Savarese, S., Fei-Fei, L., and Niebles, J. C · 2019
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Rlbench: The robot learning benchmark & learning environment
James, S., Ma, Z., Arrojo, D. R., and Davison, A. J · 2019
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Decoupled weight decay regularization
Loshchilov, I. and Hutter, F · 2019
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Dota 2 with large scale deep reinforcement learning
OpenAI, Berner, C., Brockman, G., Chan, B., Cheung, V., Debiak, P., Dennison, C., Farhi, D., Fischer, Q., Hashme, S., Hesse, C., Józefowicz, R., Gray, S., Olsson, C., Pachocki, J., Petrov, M., d. O. Pinto, H. P., Raiman, J., Salimans, T., Schlatter, J., Schneider, J., Sidor, S., Sutskever, I., Tang, J., Wolski, F., and Zhang, S · 2019
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Pytorch: An imperative style, high-performance deep learning library
Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., and Chintala, S · 2019
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Habitat: A platform for embodied ai research
Savva, M., Kadian, A., Maksymets, O., Zhao, Y., Wijmans, E., Jain, B., Straub, J., Liu, J., Koltun, V., Malik, J., Parikh, D., and Batra, D · 2019
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Megatron-lm: Training multi-billion parameter language models using model parallelism
Shoeybi, M., Patwary, M., Puri, R., LeGresley, P., Casper, J., and Catanzaro, B · 2019
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Graph-structured visual imitation
Sieb, M., Xian, Z., Huang, A., Kroemer, O., and Fragkiadaki, K · 2019
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Grasping in the wild:learning 6dof closed-loop grasping from low-cost demonstrations
Song, S., Zeng, A., Lee, J., and Funkhouser, T · 2019
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Alphastar: Mastering the real-time strategy game starcraft ii
Vinyals, O., Babuschkin, I., Chung, J., Mathieu, M., Jaderberg, M., Czarnecki, W. M., Dudzik, A., Huang, A., Georgiev, P., Powell, R., et al · 2019
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Huggingface’s transformers: State-of-the-art natural language processing
Wolf, T., Debut, L., Sanh, V., Chaumond, J., Delangue, C., Moi, A., Cistac, P., Rault, T., Louf, R., Funtowicz, M., Davison, J., Shleifer, S., von Platen, P., Ma, C., Jernite, Y., Plu, J., Xu, C., Scao, T. L., Gugger, S., Drame, M., Lhoest, Q., and Rush, A. M · 2019
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Detectron2
Wu, Y., Kirillov, A., Massa, F., Lo, W.-Y., and Girshick, R · 2019
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Meta-world: A benchmark and evaluation for multi-task and meta reinforcement learning
Yu, T., Quillen, D., He, Z., Julian, R., Narayan, A., Shively, H., Bellathur, A., Hausman, K., Finn, C., and Levine, S · 2019
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Imitating interactive intelligence
Abramson, J., Ahuja, A., Barr, I., Brussee, A., Carnevale, F., Cassin, M., Chhaparia, R., Clark, S., Damoc, B., Dudzik, A., Georgiev, P., Guy, A., Harley, T., Hill, F., Hung, A., Kenton, Z., Landon, J., Lillicrap, T., Mathewson, K., Mokrá, S., Muldal, A., Santoro, A., Savinov, N., Varma, V., Wayne, G., Williams, D., Wong, N., Yan, C., and Zhu, R · 2020
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Rearrangement: A challenge for embodied ai
Batra, D., Chang, A. X., Chernova, S., Davison, A. J., Deng, J., Koltun, V., Levine, S., Malik, J., Mordatch, I., Mottaghi, R., Savva, M., and Su, H · 2020
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Self-supervised learning for precise pick-and-place without object model
Berscheid, L., Meißner, P., and Kröger, T · 2020
Earlier work this paper cites.
Language models are few-shot learners
Brown, T. B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D. M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., and Amodei, D · 2020
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Transformers for one-shot visual imitation
Dasari, S. and Gupta, A · 2020
Earlier work this paper cites.
Self-supervised goal-conditioned pick and place
Devin, C., Rowghanian, P., Vigorito, C., Richards, W., and Rohanimanesh, K · 2020
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An image is worth 16x16 words: Transformers for image recognition at scale
Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., Uszkoreit, J., and Houlsby, N · 2020
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Big transfer (bit): General visual representation learning
Kolesnikov, A., Beyer, L., Zhai, X., Puigcerver, J., Yung, J., Gelly, S., and Houlsby, N · 2020
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12-in-1: Multi-task vision and language representation learning
Lu, J., Goswami, V., Rohrbach, M., Parikh, D., and Lee, S · 2020
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NVISII: Nvidia scene imaging interface, 2020
Morrical, N., Tremblay, J., Birchfield, S., and Wald, I · 2020
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Exploring the limits of transfer learning with a unified text-to-text transformer
Raffel, C., Shazeer, N., Roberts, A., Lee, K., Narang, S., Matena, M., Zhou, Y., Li, W., and Liu, P. J · 2020
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Recent advances in robot learning from demonstration
Ravichandar, H., Polydoros, A. S., Chernova, S., and Billard, A · 2020
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Glu variants improve transformer
Shazeer, N · 2020
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igibson 1.0: a simulation environment for interactive tasks in large realistic scenes
Shen, B., Xia, F., Li, C., Martín-Martín, R., Fan, L., Wang, G., Pérez-D’Arpino, C., Buch, S., Srivastava, S., Tchapmi, L. P., Tchapmi, M. E., Vainio, K., Wong, J., Fei-Fei, L., and Savarese, S · 2020
Cited alongside, same era.
ALFRED: A benchmark for interpreting grounded instructions for everyday tasks
Shridhar, M., Thomason, J., Gordon, D., Bisk, Y., Han, W., Mottaghi, R., Zettlemoyer, L., and Fox, D · 2020
Cited alongside, same era.
Learning to be safe: Deep rl with a safety critic
Srinivasan, K., Eysenbach, B., Ha, S., Tan, J., and Finn, C · 2020
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Efficient transformers: A survey
Tay, Y., Dehghani, M., Bahri, D., and Metzler, D · 2020
Cited alongside, same era.
The MAGICAL benchmark for robust imitation
Toyer, S., Shah, R., Critch, A., and Russell, S · 2020
The task specification problem
Agrawal, P · 2022
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Do as i can, not as i say: Grounding language in robotic affordances
Ahn, M., Brohan, A., Brown, N., Chebotar, Y., Cortes, O., David, B., Finn, C., Gopalakrishnan, K., Hausman, K., Herzog, A., Ho, D., Hsu, J., Ibarz, J., Ichter, B., Irpan, A., Jang, E., Ruano, R. J., Jeffrey, K., Jesmonth, S., Joshi, N. J., Julian, R., Kalashnikov, D., Kuang, Y., Lee, K.-H., Levine, S., Lu, Y., Luu, L., Parada, C., Pastor, P., Quiambao, J., Rao, K., Rettinghouse, J., Reyes, D., Sermanet, P., Sievers, N., Tan, C., Toshev, A., Vanhoucke, V., Xia, F., Xiao, T., Xu, P., Xu, S., and Yan, M · 2022
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Flamingo: a visual language model for few-shot learning
Alayrac, J.-B., Donahue, J., Luc, P., Miech, A., Barr, I., Hasson, Y., Lenc, K., Mensch, A., Millican, K., Reynolds, M., Ring, R., Rutherford, E., Cabi, S., Han, T., Gong, Z., Samangooei, S., Monteiro, M., Menick, J., Borgeaud, S., Brock, A., Nematzadeh, A., Sharifzadeh, S., Binkowski, M., Barreira, R., Vinyals, O., Zisserman, A., and Simonyan, K · 2022
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Video pretraining (vpt): Learning to act by watching unlabeled online videos
Baker, B., Akkaya, I., Zhokhov, P., Huizinga, J., Tang, J., Ecoffet, A., Houghton, B., Sampedro, R., and Clune, J · 2022
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Rt-1: Robotics transformer for real-world control at scale
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Transporter networks: Rearranging the visual world for robotic manipulation
Zeng, A., Florence, P., Tompson, J., Welker, S., Chien, J., Attarian, M., Armstrong, T., Krasin, I., Duong, D., Wahid, A., Sindhwani, V., and Lee, J · 2020
Cited alongside, same era.
robosuite: A modular simulation framework and benchmark for robot learning
Zhu, Y., Wong, J., Mandlekar, A., and Martín-Martín, R · 2020
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A differentiable recipe for learning visual non-prehensile planar manipulation
Aceituno, B., Rodriguez, A., Tulsiani, S., Gupta, A., and Mukadam, M · 2021
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Causalworld: A robotic manipulation benchmark for causal structure and transfer learning
Ahmed, O., Träuble, F., Goyal, A., Neitz, A., Wuthrich, M., Bengio, Y., Schölkopf, B., and Bauer, S · 2021
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Conservative safety critics for exploration
Bharadhwaj, H., Kumar, A., Rhinehart, N., Levine, S., Shkurti, F., and Garg, A · 2021
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On the opportunities and risks of foundation models
Bommasani, R., Hudson, D. A., Adeli, E., Altman, R., Arora, S., von Arx, S., Bernstein, M. S., Bohg, J., Bosselut, A., Brunskill, E., Brynjolfsson, E., Buch, S., Card, D., Castellon, R., Chatterji, N., Chen, A., Creel, K., Davis, J. Q., Demszky, D., Donahue, C., Doumbouya, M., Durmus, E., Ermon, S., Etchemendy, J., Ethayarajh, K., Fei-Fei, L., Finn, C., Gale, T., Gillespie, L., Goel, K., Goodman, N., Grossman, S., Guha, N., Hashimoto, T., Henderson, P., Hewitt, J., Ho, D. E., Hong, J., Hsu, K., Huang, J., Icard, T., Jain, S., Jurafsky, D., Kalluri, P., Karamcheti, S., Keeling, G., Khani, F., Khattab, O., Koh, P. W., Krass, M., Krishna, R., Kuditipudi, R., Kumar, A., Ladhak, F., Lee, M., Lee, T., Leskovec, J., Levent, I., Li, X. L., Li, X., Ma, T., Malik, A., Manning, C. D., Mirchandani, S., Mitchell, E., Munyikwa, Z., Nair, S., Narayan, A., Narayanan, D., Newman, B., Nie, A., Niebles, J. C., Nilforoshan, H., Nyarko, J., Ogut, G., Orr, L., Papadimitriou, I., Park, J. S., Piech, C., Portelance, E., Potts, C., Raghunathan, A., Reich, R., Ren, H., Rong, F., Roohani, Y., Ruiz, C., Ryan, J., Ré, C., Sadigh, D., Sagawa, S., Santhanam, K., Shih, A., Srinivasan, K., Tamkin, A., Taori, R., Thomas, A. W., Tramèr, F., Wang, R. E., Wang, W., Wu, B., Wu, J., Wu, Y., Xie, S. M., Yasunaga, M., You, J., Zaharia, M., Zhang, M., Zhang, T., Zhang, X., Zhang, Y., Zheng, L., Zhou, K., and Liang, P · 2021
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Brohan, A., Brown, N., Carbajal, J., Chebotar, Y., Dabis, J., Finn, C., Gopalakrishnan, K., Hausman, K., Herzog, A., Hsu, J., Ibarz, J., Ichter, B., Irpan, A., Jackson, T., Jesmonth, S., Joshi, N. J., Julian, R., Kalashnikov, D., Kuang, Y., Leal, I., Lee, K.-H., Levine, S., Lu, Y., Malla, U., Manjunath, D., Mordatch, I., Nachum, O., Parada, C., Peralta, J., Perez, E., Pertsch, K., Quiambao, J., Rao, K., Ryoo, M., Salazar, G., Sanketi, P., Sayed, K., Singh, J., Sontakke, S., Stone, A., Tan, C., Tran, H., Vanhoucke, V., Vega, S., Vuong, Q., Xia, F., Xiao, T., Xu, P., Xu, S., Yu, T., and Zitkovich, B · 2022
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Revisiting the ”video” in video-language understanding
Buch, S., Eyzaguirre, C., Gaidon, A., Wu, J., Fei-Fei, L., and Niebles, J. C · 2022
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Latte: Language trajectory transformer
Bucker, A., Figueredo, L., Haddadin, S., Kapoor, A., Ma, S., Vemprala, S., and Bonatti, R · 2022
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Pix2seq: A language modeling framework for object detection
Chen, T., Saxena, S., Li, L., Fleet, D. J., and Hinton, G. E · 2022
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Palm: Scaling language modeling with pathways
Chowdhery, A., Narang, S., Devlin, J., Bosma, M., Mishra, G., Roberts, A., Barham, P., Chung, H. W., Sutton, C., Gehrmann, S., Schuh, P., Shi, K., Tsvyashchenko, S., Maynez, J., Rao, A., Barnes, P., Tay, Y., Shazeer, N., Prabhakaran, V., Reif, E., Du, N., Hutchinson, B., Pope, R., Bradbury, J., Austin, J., Isard, M., Gur-Ari, G., Yin, P., Duke, T., Levskaya, A., Ghemawat, S., Dev, S., Michalewski, H., Garcia, X., Misra, V., Robinson, K., Fedus, L., Zhou, D., Ippolito, D., Luan, D., Lim, H., Zoph, B., Spiridonov, A., Sepassi, R., Dohan, D., Agrawal, S., Omernick, M., Dai, A. M., Pillai, T. S., Pellat, M., Lewkowycz, A., Moreira, E., Child, R., Polozov, O., Lee, K., Zhou, Z., Wang, X., Saeta, B., Diaz, M., Firat, O., Catasta, M., Wei, J., Meier-Hellstern, K., Eck, D., Dean, J., Petrov, S., and Fiedel, N · 2022
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From play to policy: Conditional behavior generation from uncurated robot data
Cui, Z. J., Wang, Y., Shafiullah, N. M. M., and Pinto, L · 2022
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Procthor: Large-scale embodied ai using procedural generation
Deitke, M., VanderBilt, E., Herrasti, A., Weihs, L., Salvador, J., Ehsani, K., Han, W., Kolve, E., Farhadi, A., Kembhavi, A., and Mottaghi, R · 2022
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Google scanned objects: A high-quality dataset of 3d scanned household items
Downs, L., Francis, A., Koenig, N., Kinman, B., Hickman, R., Reymann, K., McHugh, T. B., and Vanhoucke, V · 2022
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A survey of embodied AI: from simulators to research tasks
Duan, J., Yu, S., Tan, H. L., Zhu, H., and Tan, C · 2022
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Minedojo: Building open-ended embodied agents with internet-scale knowledge
Fan, L., Wang, G., Jiang, Y., Mandlekar, A., Yang, Y., Zhu, H., Tang, A., Huang, D.-A., Zhu, Y., and Anandkumar, A · 2022
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On pre-training for visuo-motor control: Revisiting a learning-from-scratch baseline
Hansen, N., Yuan, Z., Ze, Y., Mu, T., Rajeswaran, A., Su, H., Xu, H., and Wang, X · 2022
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Language models as zero-shot planners: Extracting actionable knowledge for embodied agents
Huang, W., Abbeel, P., Pathak, D., and Mordatch, I · 2022
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Uvim: A unified modeling approach for vision with learned guiding codes
Kolesnikov, A., Pinto, A. S., Beyer, L., Zhai, X., Harmsen, J., and Houlsby, N · 2022
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Unified-io: A unified model for vision, language, and multi-modal tasks
Lu, J., Clark, C., Zellers, R., Mottaghi, R., and Kembhavi, A · 2022
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Vip: Towards universal visual reward and representation via value-implicit pre-training
Ma, Y. J., Sodhani, S., Jayaraman, D., Bastani, O., Kumar, V., and Zhang, A · 2022
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Simple open-vocabulary object detection with vision transformers
Minderer, M., Gritsenko, A., Stone, A., Neumann, M., Weissenborn, D., Dosovitskiy, A., Mahendran, A., Arnab, A., Dehghani, M., Shen, Z., Wang, X., Zhai, X., Kipf, T., and Houlsby, N · 2022
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R3m: A universal visual representation for robot manipulation
Nair, S., Rajeswaran, A., Kumar, V., Finn, C., and Gupta, A · 2022
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Real-world robot learning with masked visual pre-training
Radosavovic, I., Xiao, T., James, S., Abbeel, P., Malik, J., and Darrell, T · 2022
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A generalist agent
Reed, S., Zolna, K., Parisotto, E., Colmenarejo, S. G., Novikov, A., Barth-Maron, G., Gimenez, M., Sulsky, Y., Kay, J., Springenberg, J. T., Eccles, T., Bruce, J., Razavi, A., Edwards, A., Heess, N., Chen, Y., Hadsell, R., Vinyals, O., Bordbar, M., and de Freitas, N · 2022
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Can wikipedia help offline reinforcement learning?
Reid, M., Yamada, Y., and Gu, S. S · 2022
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Behavior transformers: Cloning k k modes with one stone
Shafiullah, N. M. M., Cui, Z. J., Altanzaya, A., and Pinto, L · 2022
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Lm-nav: Robotic navigation with large pre-trained models of language, vision, and action
Shah, D., Osinski, B., Ichter, B., and Levine, S · 2022
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Perceiver-actor: A multi-task transformer for robotic manipulation
Shridhar, M., Manuelli, L., and Fox, D · 2022
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Guiding multi-step rearrangement tasks with natural language instructions
Stengel-Eskin, E., Hundt, A., He, Z., Murali, A., Gopalan, N., Gombolay, M., and Hager, G · 2022
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Blender online libraries for textures, 2022
Weikert, A., Goralczyk, A., Salmela, B., Dansie, B., Barton, C., Valenza, E., Alexandrov, G., Hubert, I., Tysdal, K., Sokolowski, L., Järvinen, M., Pulieso, M., Ebb, M., Vazquez, P., Tuytel, R., Hess, R., Feldlaufer, S., König, S., Platen, S., and Mäter., S · 2022
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Masked visual pre-training for motor control
Xiao, T., Radosavovic, I., Darrell, T., and Malik, J · 2022
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Prompting decision transformer for few-shot policy generalization
Xu, M., Shen, Y., Zhang, S., Lu, Y., Zhao, D., Tenenbaum, J. B., and Gan, C · 2022
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i-code: An integrative and composable multimodal learning framework
Yang, Z., Fang, Y., Zhu, C., Pryzant, R., Chen, D., Shi, Y., Xu, Y., Qian, Y., Gao, M., Chen, Y.-L., Lu, L., Xie, Y., Gmyr, R., Codella, N., Kanda, N., Xiao, B., Yuan, L., Yoshioka, T., Zeng, M., and Huang, X · 2022
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Merlot reserve: Neural script knowledge through vision and language and sound
Zellers, R., Lu, J., Lu, X., Yu, Y., Zhao, Y., Salehi, M., Kusupati, A., Hessel, J., Farhadi, A., and Choi, Y · 2022
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Socratic models: Composing zero-shot multimodal reasoning with language
Zeng, A., Wong, A., Welker, S., Choromanski, K., Tombari, F., Purohit, A., Ryoo, M., Sindhwani, V., Lee, J., Vanhoucke, V., and Florence, P · 2022
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Towards more generalizable one-shot visual imitation learning
Zhao, M., Liu, F., Lee, K., and Abbeel, P · 2022
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Online decision transformer
Zheng, Q., Zhang, A., and Grover, A · 2022
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Palm-e: An embodied multimodal language model
Driess, D., Xia, F., Sajjadi, M. S. M., Lynch, C., Chowdhery, A., Ichter, B., Wahid, A., Tompson, J., Vuong, Q., Yu, T., Huang, W., Chebotar, Y., Sermanet, P., Duckworth, D., Levine, S., Vanhoucke, V., Hausman, K., Toussaint, M., Greff, K., Zeng, A., Mordatch, I., and Florence, P · 2023
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Segment anything
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