Zdroje a referencie

Prehľadové články a súvisiace práce

Lee, Zhao, Sawhney, Girdhar & Kroemer (2021): Lee, T., Zhao, J., Sawhney, A., Girdhar, S. & Kroemer, O. (2021). Causal reasoning in simulation for structure and transfer learning of robot manipulation policies. IEEE. https://doi.org/10.1109/icra48506.2021.9561439
Gerstenberg & Tenenbaum (2017): Gerstenberg, T. & Tenenbaum, J. (2017). Intuitive theories. InWaldmann, M. (Eds.), The oxford handbook of causal reasoning. (pp. 515–548). Oxford University Press. https://doi.org/10.1093/oxfordhb/9780199399550.013.28
Hellström (2021): Hellström, T. (2021). The relevance of causation in robotics: A review, categorization, and analysis. Paladyn, Journal of Behavioral Robotics, 12(1). 238–255. https://doi.org/10.1515/pjbr-2021-0017
Lundberg & Lee (2017): Lundberg, S. & Lee, S. (2017). A unified approach to interpreting model predictions.
Ribeiro, Singh & Guestrin (2016): Ribeiro, M., Singh, S. & Guestrin, C. (2016). “Why should I trust you?”: Explaining the predictions of any classifier. Association for Computing Machinery. https://doi.org/10.1145/2939672.2939778
Vavrečka, Sokovnin, Mejdrechová & Šejnová (2021): Vavrečka, M., Sokovnin, N., Mejdrechová, M. & Šejnová, G. (2021). MyGym: Modular toolkit for visuomotor robotic tasks. IEEE. https://doi.org/10.1109/ictai52525.2021.00046
Lake, Ullman, Tenenbaum & Gershman (2016): Lake, B., Ullman, T., Tenenbaum, J. & Gershman, S. (2016). Building machines that learn and think like people. Behavioral and Brain Sciences, 40. https://doi.org/10.1017/s0140525x16001837
Zhang, Schölkopf, Spirtes & Glymour (2017): Zhang, K., Schölkopf, B., Spirtes, P. & Glymour, C. (2017). Learning causality and causality-related learning: Some recent progress. National Science Review, 5(1). 26–29. https://doi.org/10.1093/nsr/nwx137
Schölkopf (2022): Schölkopf, B. (2022). Causality for machine learning. In Probabilistic and causal inference: The works of judea pearl. (1, pp. 765–804). Association for Computing Machinery. https://doi.org/10.1145/3501714.3501755
Li (2023): Li, Y. (2023). Deep causal learning for robotic intelligence. Frontiers in Neurorobotics, 17. https://doi.org/10.3389/fnbot.2023.1128591
Shrikumar, Greenside & Kundaje (2017): Shrikumar, A., Greenside, P. & Kundaje, A. (2017). Learning important features through propagating activation differences. PMLR.
Kingma & Ba (2014): Kingma, D. & Ba, J. (2014). Adam: A method for stochastic optimization. https://doi.org/10.48550/ARXIV.1412.6980
Loshchilov & Hutter (2017): Loshchilov, I. & Hutter, F. (2017). Decoupled weight decay regularization. https://doi.org/10.48550/arXiv.1711.05101
Lombard & Gärdenfors (2017): Lombard, M. & Gärdenfors, P. (2017). Tracking the evolution of causal cognition in humans. Journal of Anthropological Sciences, 95. 219–234. https://doi.org/10.4436/JASS.95006
Gärdenfors & Lombard (2018): Gärdenfors, P. & Lombard, M. (2018). Causal cognition, force dynamics and early hunting technologies. Frontiers in Psychology, 9. https://doi.org/10.3389/fpsyg.2018.00087
Pearl (2009): Pearl, J. (2009). Causality: Models, reasoning, and inference (2). Cambridge University Press. https://doi.org/10.1017/cbo9780511803161
Peters, Janzing & Bernard (2017): Peters, J., Janzing, D. & Bernard, S. (2017). Elements of causal inference – foundations and learning algorithms. MIT Press.
Zhu, Gao, Fan, Huang, Edmonds, Liu, Gao, Zhang, Qi, Wu, Tenenbaum & Zhu (2020): Zhu, Y., Gao, T., Fan, L., Huang, S., Edmonds, M., Liu, H., Gao, F., Zhang, C., Qi, S., Wu, Y., Tenenbaum, J. & Zhu, S. (2020). Dark, beyond deep: A paradigm shift to cognitive AI with humanlike common sense. Engineering, 6(3). 310–345. https://doi.org/10.1016/j.eng.2020.01.011
Nouri & Littman (2010): Nouri, A. & Littman, M. (2010). Dimension reduction and its application to model-based exploration in continuous spaces. Machine Learning, 81(1). 85–98. https://doi.org/10.1007/s10994-010-5202-y
Dearden & Demiris (2005): Dearden, A. & Demiris, Y. (2005). Learning forward models for robots. Morgan Kaufmann Publishers Inc..
Gilpin, Bau, Yuan, Bajwa, Specter & Kagal (2018): Gilpin, L., Bau, D., Yuan, B., Bajwa, A., Specter, M. & Kagal, L. (2018). Explaining explanations: An overview of interpretability of machine learning. IEEE. https://doi.org/10.1109/dsaa.2018.00018
Wolpert & Kawato (1998): Wolpert, D. & Kawato, M. (1998). Multiple paired forward and inverse models for motor control. Neural Networks, 11(7–8). 1317–1329. https://doi.org/10.1016/s0893-6080(98)00066-5
Ciria, Schillaci, Pezzulo, Hafner & Lara (2021): Ciria, A., Schillaci, G., Pezzulo, G., Hafner, V. & Lara, B. (2021). Predictive processing in cognitive robotics: A review. Neural Computation, 33(5). 1402–1432. https://doi.org/10.1162/neco\_a\_01383
Dillon, LaRiviere, Lundberg, Roth & Syrgkanis (2021): Dillon, E., LaRiviere, J., Lundberg, S., Roth, J. & Syrgkanis, V. (2021). Be careful when interpreting predictive models in search of causal insights. Medium. Retrieved from https://towardsdatascience.com/be-careful-when-interpreting-predictive-models-in-search-of-causal-insights-e68626e664b6
Friedman (2001): Friedman, J. (2001). Greedy function approximation: A gradient boosting machine. The Annals of Statistics, 29(5). 1189–1232. https://doi.org/10.1214/aos/1013203451
Lake (2014): Lake, B. (2014). Towards more human-like concept learning in machines: Compositionality, causality, and learning-to-learn ({PhD thesis}). Massachusetts Institute of Technology Retrieved from https://dspace.mit.edu/handle/1721.1/95856
Nguyen-Tuong & Peters (2011): Nguyen-Tuong, D. & Peters, J. (2011). Model learning for robot control: A survey. Cognitive Processing, 12(4). 319–340. https://doi.org/10.1007/s10339-011-0404-1
Kotseruba & Tsotsos (2018): Kotseruba, I. & Tsotsos, J. (2018). 40 years of cognitive architectures: Core cognitive abilities and practical applications. Artificial Intelligence Review, 53(1). 17–94. https://doi.org/10.1007/s10462-018-9646-y
Rosenblatt (1958): Rosenblatt, F. (1958). The Perceptron: A probabilistic model for information storage and organization in the brain.. Psychological Review, 65(6). 386–408. https://doi.org/10.1037/h0042519
McClelland, Rumelhart & Hinton (1988): McClelland, J., Rumelhart, D. & Hinton, G. (1988). The appeal of parallel distributed processing. In Readings in cognitive science. (pp. 52–72). Elsevier. https://doi.org/10.1016/b978-1-4832-1446-7.50010-8
McClelland, Rumelhart & PDP Research Group (1987): McClelland, J., Rumelhart, D. & PDP Research Group (1987). Parallel distributed processing: Explorations in the microstructure of cognition, volume 2: Psychological and biological models: Psychological and biological models. The MIT Press.
Rumelhart, Hinton & Williams (1986): Rumelhart, D., Hinton, G. & Williams, R. (1986). Learning representations by back-propagating errors. Nature, 323(6088). 533–536. https://doi.org/10.1038/323533a0
Hornik, Stinchcombe & White (1989): Hornik, K., Stinchcombe, M. & White, H. (1989). Multilayer feedforward networks are universal approximators. Neural Networks, 2(5). 359–366. https://doi.org/10.1016/0893-6080(89)90020-8
Rosenblatt & (1962): Rosenblatt, F. & (1962). Principles of neurodynamics: Perceptrons and the theory of brain mechanisms. Spartan Books.
Minsky & Papert (2017): Minsky, M. & Papert, S. (2017). Perceptrons: An introduction to computational geometry. The MIT Press. https://doi.org/10.7551/mitpress/11301.001.0001
Shapley (1953): Shapley, L. (1953). A value for n-person games. InKuhn, H. & Tucker, A. (Eds.), Contributions to the theory of games II. (pp. 307–317). Princeton University Press. https://doi.org/10.1515/9781400881970-018
Matsui & Matsui (2001): Matsui, Y. & Matsui, T. (2001). NP-completeness for calculating power indices of weighted majority games. Theoretical Computer Science, 263(1–2). 305–310. https://doi.org/10.1016/s0304-3975(00)00251-6
Hochreiter & Schmidhuber (1997): Hochreiter, S. & Schmidhuber, J. (1997). Long short-term memory. Neural Computation, 9(8). 1735–1780. https://doi.org/10.1162/neco.1997.9.8.1735
Chen, Lu, Rajeswaran, Lee, Grover, Laskin, Abbeel, Srinivas & Mordatch (2021): Chen, L., Lu, K., Rajeswaran, A., Lee, K., Grover, A., Laskin, M., Abbeel, P., Srinivas, A. & Mordatch, I. (2021). Decision Transformer: Reinforcement learning via sequence modeling. Curran Associates, Inc..
Janner, Li & Levine (2021): Janner, M., Li, Q. & Levine, S. (2021). Offline reinforcement learning as one big sequence modeling problem. Curran Associates, Inc..
Vaswani, Shazeer, Parmar, Uszkoreit, Jones, Gomez, Kaiser & Polosukhin (2017): Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A., Kaiser, Ł. & Polosukhin, I. (2017). Attention is all you need. Curran Associates, Inc..
Ghosh, Gupta, Reddy, Fu, Devin, Eysenbach & Levine (2019): Ghosh, D., Gupta, A., Reddy, A., Fu, J., Devin, C., Eysenbach, B. & Levine, S. (2019). Learning to reach goals via iterated supervised learning. https://doi.org/10.48550/ARXIV.1912.06088
Oh, Guo, Singh & Lee (2018): Oh, J., Guo, Y., Singh, S. & Lee, H. (2018). Self-imitation learning. PMLR.
Fisher (1925): Fisher, R. (1925). Statistical methods for research workers. Oliver; Boyd.
Diehl & Ramirez-Amaro (2023): Diehl, M. & Ramirez-Amaro, K. (2023). A causal-based approach to explain, predict and prevent failures in robotic tasks. Robotics and Autonomous Systems, 162. 104376. https://doi.org/10.1016/j.robot.2023.104376
Lee, Vats, Girdhar & Kroemer (2023): Lee, T., Vats, S., Girdhar, S. & Kroemer, O. (2023). SCALE: Causal learning and discovery of robot manipulation skills using simulation. PMLR.
Stocking, Gopnik & Tomlin (2022): Stocking, K., Gopnik, A. & Tomlin, C. (2022). From robot learning to robot understanding: Leveraging causal graphical models for robotics. PMLR.
Pearl (1985): Pearl, J. (1985). Bayesian networks: A model of self-activated memory for evidential reasoning.
Sontakke, Mehrjou, Itti & Schölkopf (2021): Sontakke, S., Mehrjou, A., Itti, L. & Schölkopf, B. (2021). Causal curiosity: RL agents discovering self-supervised experiments for causal representation learning. PMLR.
Sonar, Pacelli & Majumdar (2021): Sonar, A., Pacelli, V. & Majumdar, A. (2021). Invariant policy optimization: Towards stronger generalization in reinforcement learning. PMLR.
Wang, Xiao, Xu, Zhu & Stone (2022): Wang, Z., Xiao, X., Xu, Z., Zhu, Y. & Stone, P. (2022). Causal dynamics learning for task-independent state abstraction. PMLR.
Brandfonbrener, Bietti, Buckman, Laroche & Bruna (2022): Brandfonbrener, D., Bietti, A., Buckman, J., Laroche, R. & Bruna, J. (2022). When does return-conditioned supervised learning work for offline reinforcement learning?. Curran Associates, Inc..
Furuta, Matsuo & Gu (2021): Furuta, H., Matsuo, Y. & Gu, S. (2021). Generalized decision transformer for offline hindsight information matching. https://doi.org/10.48550/ARXIV.2111.10364
Emmons, Eysenbach, Kostrikov & Levine (2021): Emmons, S., Eysenbach, B., Kostrikov, I. & Levine, S. (2021). RvS: What is essential for offline RL via supervised learning?. https://doi.org/10.48550/ARXIV.2112.10751
Wen, Kuba, Lin, Zhang, Wen, Wang & Yang (2022): Wen, M., Kuba, J., Lin, R., Zhang, W., Wen, Y., Wang, J. & Yang, Y. (2022). Multi-agent reinforcement learning is a sequence modeling problem. Curran Associates, Inc..
Zare, Kebria, Khosravi & Nahavandi (2023): Zare, M., Kebria, P., Khosravi, A. & Nahavandi, S. (2023). A survey of imitation learning: Algorithms, recent developments, and challenges. https://doi.org/10.48550/ARXIV.2309.02473
Mandlekar, Xu, Wong, Nasiriany, Wang, Kulkarni, Fei-Fei, Savarese, Zhu & Martín-Martín (2021): Mandlekar, A., Xu, D., Wong, J., Nasiriany, S., Wang, C., Kulkarni, R., Fei-Fei, L., Savarese, S., Zhu, Y. & Martín-Martín, R. (2021). What matters in learning from offline human demonstrations for robot manipulation. https://doi.org/10.48550/ARXIV.2108.03298
Dogge, Custers & Aarts (2019): Dogge, M., Custers, R. & Aarts, H. (2019). Moving forward: On the limits of motor-based forward models. Trends in Cognitive Sciences, 23(9). 743–753. https://doi.org/10.1016/j.tics.2019.06.008
Francis & Wonham (1976): Francis, B. & Wonham, W. (1976). The internal model principle of control theory. Automatica, 12(5). 457–465. https://doi.org/10.1016/0005-1098(76)90006-6
Wolpert & Flanagan (2001): Wolpert, D. & Flanagan, J. (2001). Motor prediction. Current Biology, 11(18). R729–R732. https://doi.org/10.1016/s0960-9822(01)00432-8
Miall & Wolpert (1996): Miall, R. & Wolpert, D. (1996). Forward models for physiological motor control. Neural Networks, 9(8). 1265–1279. https://doi.org/10.1016/s0893-6080(96)00035-4
Sperry (1950): Sperry, R. (1950). Neural basis of the spontaneous optokinetic response produced by visual inversion.. Journal of Comparative and Physiological Psychology, 43(6). 482–489. https://doi.org/10.1037/h0055479
Holst & Mittelstaedt (1950): Holst, E. & Mittelstaedt, H. (1950). Das Reafferenzprinzip: Wechselwirkungen zwischen Zentralnervensystem und Peripherie. Naturwissenschaften, 37(20). 464–476. https://doi.org/10.1007/bf00622503

Technická dokumentácia k použitým nástrojom

Nástroje pre robotiku
- myGym documentation
- myGym repository
Knižnice pre strojové učenie
Knižnice pre akcelerované počítanie
Vizualizačné nástroje