{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,5]],"date-time":"2026-03-05T14:29:49Z","timestamp":1772720989141,"version":"3.50.1"},"reference-count":38,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2022,3,28]],"date-time":"2022-03-28T00:00:00Z","timestamp":1648425600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Entropy"],"abstract":"<jats:p>We show that goal-directed action planning and generation in a teleological framework can be formulated by extending the active inference framework. The proposed model, which is built on a variational recurrent neural network model, is characterized by three essential features. These are that (1) goals can be specified for both static sensory states, e.g., for goal images to be reached and dynamic processes, e.g., for moving around an object, (2) the model cannot only generate goal-directed action plans, but can also understand goals through sensory observation, and (3) the model generates future action plans for given goals based on the best estimate of the current state, inferred from past sensory observations. The proposed model is evaluated by conducting experiments on a simulated mobile agent as well as on a real humanoid robot performing object manipulation.<\/jats:p>","DOI":"10.3390\/e24040469","type":"journal-article","created":{"date-parts":[[2022,3,29]],"date-time":"2022-03-29T21:44:52Z","timestamp":1648590292000},"page":"469","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["Goal-Directed Planning and Goal Understanding by Extended Active Inference: Evaluation through Simulated and Physical Robot Experiments"],"prefix":"10.3390","volume":"24","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1388-2906","authenticated-orcid":false,"given":"Takazumi","family":"Matsumoto","sequence":"first","affiliation":[{"name":"Cognitive Neurorobotics Research Unit, Okinawa Institute of Science and Technology, Okinawa 904-0495, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2590-8982","authenticated-orcid":false,"given":"Wataru","family":"Ohata","sequence":"additional","affiliation":[{"name":"Cognitive Neurorobotics Research Unit, Okinawa Institute of Science and Technology, Okinawa 904-0495, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4083-4512","authenticated-orcid":false,"given":"Fabien C. Y.","family":"Benureau","sequence":"additional","affiliation":[{"name":"Cognitive Neurorobotics Research Unit, Okinawa Institute of Science and Technology, Okinawa 904-0495, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9131-9206","authenticated-orcid":false,"given":"Jun","family":"Tani","sequence":"additional","affiliation":[{"name":"Cognitive Neurorobotics Research Unit, Okinawa Institute of Science and Technology, Okinawa 904-0495, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,3,28]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"155","DOI":"10.7551\/mitpress\/1753.003.0010","article-title":"Goal-directed action and teleological explanation","volume":"4","author":"Sehon","year":"2007","journal-title":"Causation Explan."},{"key":"ref_2","first-page":"17","article-title":"Actions, Reason Explanations, and Values","volume":"1","year":"2016","journal-title":"Tutti Diritti Riserv."},{"key":"ref_3","first-page":"111","article-title":"One-year-old infants use teleological representations of actions productively","volume":"27","author":"Csibra","year":"2003","journal-title":"Cogn. Sci."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"275","DOI":"10.1007\/BF00201442","article-title":"Trajectory formation of arm movement by cascade neural network model based on minimum torque-change criterion","volume":"62","author":"Kawato","year":"1990","journal-title":"Biol. Cybern."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1265","DOI":"10.1016\/S0893-6080(96)00035-4","article-title":"Forward models for physiological motor control","volume":"9","author":"Miall","year":"1996","journal-title":"Neural Netw."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"718","DOI":"10.1016\/S0959-4388(99)00028-8","article-title":"Internal models for motor control and trajectory planning","volume":"9","author":"Kawato","year":"1999","journal-title":"Curr. Opin. Neurobiol."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"187","DOI":"10.1080\/17588928.2015.1020053","article-title":"Active inference and epistemic value","volume":"6","author":"Friston","year":"2015","journal-title":"Cogn. Neurosci."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"495","DOI":"10.1007\/s00422-019-00805-w","article-title":"Generalised free energy and active inference","volume":"113","author":"Parr","year":"2019","journal-title":"Biol. Cybern."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"137","DOI":"10.1007\/s00422-011-0424-z","article-title":"Action understanding and active inference","volume":"104","author":"Friston","year":"2011","journal-title":"Biol. Cybern."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"523","DOI":"10.1007\/s00422-012-0512-8","article-title":"Active inference and agency: Optimal control without cost functions","volume":"106","author":"Friston","year":"2012","journal-title":"Biol. Cybern."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Baltieri, M., and Buckley, C.L. (2017, January 4\u20138). An active inference implementation of phototaxis. Proceedings of the 14th European Conference on Artificial Life ECAL 2017, Lyon, France.","DOI":"10.7551\/ecal_a_011"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"815","DOI":"10.1098\/rstb.2005.1622","article-title":"A theory of cortical responses","volume":"360","author":"Friston","year":"2005","journal-title":"Philos. Trans. R. Soc. B Biol. Sci."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Matsumoto, T., and Tani, J. (2020). Goal-Directed Planning for Habituated Agents by Active Inference Using a Variational Recurrent Neural Network. Entropy, 22.","DOI":"10.3390\/e22050564"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"2025","DOI":"10.1162\/neco_a_01228","article-title":"A novel predictive-coding-inspired variational RNN model for online prediction and recognition","volume":"31","author":"Ahmadi","year":"2019","journal-title":"Neural Comput."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"196","DOI":"10.3389\/fpsyg.2014.00196","article-title":"Postdiction: Its implications on visual awareness, hindsight, and sense of agency","volume":"5","author":"Shimojo","year":"2014","journal-title":"Front. Psychol."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1016\/S0893-6080(02)00214-9","article-title":"Learning to generate articulated behavior through the bottom-up and the top-down interaction processes","volume":"16","author":"Tani","year":"2003","journal-title":"Neural Netw."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"421","DOI":"10.1109\/3477.499793","article-title":"Model-based learning for mobile robot navigation from the dynamical systems perspective","volume":"26","author":"Tani","year":"1996","journal-title":"IEEE Trans. Syst. Man Cybern. Part B"},{"key":"ref_18","first-page":"11662","article-title":"Deep active inference agents using Monte-Carlo methods","volume":"33","author":"Fountas","year":"2020","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_19","unstructured":"Sajid, N., Tigas, P., Zakharov, A., Fountas, Z., and Friston, K. (2021). Exploration and preference satisfaction trade-off in reward-free learning. arXiv."},{"key":"ref_20","first-page":"103","article-title":"Learning Generative State Space Models for Active Inference","volume":"14","author":"Wauthier","year":"2020","journal-title":"Front. Comput. Neurosci."},{"key":"ref_21","unstructured":"Hafner, D., Lillicrap, T., Ba, J., and Norouzi, M. (2019). Dream to control: Learning behaviors by latent imagination. arXiv."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"307","DOI":"10.1142\/S1793005709001283","article-title":"Creating novel goal-directed actions at criticality: A neuro-robotic experiment","volume":"5","author":"Arie","year":"2009","journal-title":"New Math. Nat. Comput."},{"key":"ref_23","unstructured":"Nasiriany, S., Pong, V., Lin, S., and Levine, S. (2019, January 8\u201314). Planning with Goal-Conditioned Policies. Proceedings of the Advances in Neural Information Processing Systems, Vancouver, BC, Canada."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Yamashita, Y., and Tani, J. (2008). Emergence of functional hierarchy in a multiple timescale neural network model: A humanoid robot experiment. PLoS Comput. Biol., 4.","DOI":"10.1371\/journal.pcbi.1000220"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Rumelhart, D., Hinton, G., and Williams, R. (1986). Learning internal representations by error propagation. Parallel Distributed Processing, MIT Press. Chapter 8.","DOI":"10.21236\/ADA164453"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"61","DOI":"10.3389\/fnbot.2020.00061","article-title":"Investigation of the Sense of Agency in Social Cognition, Based on Frameworks of Predictive Coding and Active Inference: A Simulation Study on Multimodal Imitative Interaction","volume":"14","author":"Ohata","year":"2020","journal-title":"Front. Neurorobot."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Doya, K., and Yoshizawa, S. (1989, January 18\u201322). Memorizing oscillatory patterns in the analog neuron network. Proceedings of the 1989 International Joint Conference on Neural Networks, Washington DC, USA.","DOI":"10.1109\/IJCNN.1989.118555"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"270","DOI":"10.1162\/neco.1989.1.2.270","article-title":"A learning algorithm for continually running fully recurrent neural networks","volume":"1","author":"Williams","year":"1989","journal-title":"Neural Comput."},{"key":"ref_29","unstructured":"Kingma, D.P., and Welling, M. (2014, January 14\u201316). Auto-Encoding Variational Bayes. Proceedings of the 2nd International Conference on Learning Representations, ICLR 2014, Banff, AB, Canada."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1016\/j.neunet.2017.02.015","article-title":"How Can a Recurrent Neurodynamic Predictive Coding Model Cope with Fluctuation in Temporal Patterns? Robotic Experiments on Imitative Interaction","volume":"92","author":"Ahmadi","year":"2017","journal-title":"Neural Netw."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"135","DOI":"10.1016\/j.neunet.2019.05.001","article-title":"Learning, planning, and control in a monolithic neural event inference architecture","volume":"117","author":"Butz","year":"2019","journal-title":"Neural Netw."},{"key":"ref_32","unstructured":"Redmon, J., and Farhadi, A. (2018). YOLOv3: An Incremental Improvement. arXiv."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Oliver, G., Lanillos, P., and Cheng, G. (2021). An empirical study of active inference on a humanoid robot. IEEE Trans. Cogn. Dev. Syst., 1.","DOI":"10.1109\/TCDS.2021.3049907"},{"key":"ref_34","unstructured":"Meo, C., Franzese, G., Pezzato, C., Spahn, M., and Lanillos, P. (2021). Adaptation through prediction: Multisensory active inference torque control. arXiv."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"642780","DOI":"10.3389\/fnbot.2021.642780","article-title":"Active Vision for Robot Manipulators Using the Free Energy Principle","volume":"15","author":"Verbelen","year":"2021","journal-title":"Front. Neurorobotics"},{"key":"ref_36","unstructured":"Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., and Garnett, R. (2017). Hindsight Experience Replay. Advances in Neural Information Processing Systems, Curran Associates, Inc."},{"key":"ref_37","unstructured":"Mendonca, R., Rybkin, O., Daniilidis, K., Hafner, D., and Pathak, D. (2021). Discovering and Achieving Goals via World Models. Advances in Neural Information Processing Systems, Curran Associates, Inc."},{"key":"ref_38","unstructured":"Warde-Farley, D., de Wiele, T.V., Kulkarni, T.D., Ionescu, C., Hansen, S., and Mnih, V. (2019, January 6\u20139). Unsupervised Control Through Non-Parametric Discriminative Rewards. Proceedings of the 7th International Conference on Learning Representations, ICLR 2019, New Orleans, LA, USA."}],"container-title":["Entropy"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1099-4300\/24\/4\/469\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T22:44:57Z","timestamp":1760136297000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1099-4300\/24\/4\/469"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,3,28]]},"references-count":38,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2022,4]]}},"alternative-id":["e24040469"],"URL":"https:\/\/doi.org\/10.3390\/e24040469","relation":{},"ISSN":["1099-4300"],"issn-type":[{"value":"1099-4300","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,3,28]]}}}