Action-Centric World Modeling for Long-Horizon Embodied Task Planning and Execution
Keywords:
action-centric world modeling, embodied planning, world models, action-aware memory, long-horizon control, socio-technical systems, model-based reinforcement learningAbstract
Long-horizon embodied task planning requires agents to integrate perception, memory, prediction, and control across extended periods. This paper develops a systems-level account of action-centric world modeling, which reframes learned predictive models around the causal consequences of interventions rather than passive observation or undirected exploration. Contemporary world models have advanced model-based reinforcement learning, yet many architectures remain optimized for reconstruction, curiosity, or short-term prediction. In embodied settings, agents must execute instructions, manipulate objects, recover from failures, and maintain task-relevant memory under partial observability. This paper argues that action-aware memory and interactive world modeling should be designed as coupled infrastructure rather than isolated algorithmic modules. It analyzes architectural trade-offs involving sample efficiency, latency, long-term credit assignment, uncertainty calibration, and system maintainability. The paper further considers robustness under distribution shift, compounding rollout errors, and hardware variability. Beyond technical performance, the article addresses governance, fairness, accountability, deployment sustainability, and policy implications. It proposes that action-centric world models are best understood as socio-technical infrastructures whose design choices shape safety, equity, and operational longevity. The discussion integrates perspectives from machine learning, robotics, and critical systems research to outline a forward-looking research agenda for auditable, adaptive, and sustainable embodied planning systems.
References
1. Ha, D., & Schmidhuber, J. (2018). Recurrent world models facilitate policy evolution. In Advances in Neural Information Processing Systems 31 (NeurIPS 2018).
2. LeCun, Y. (2022). A path towards autonomous machine intelligence version 0.9.2. OpenReview. https://openreview.net/forum?id=BZ5a1r-kVsf
3. Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A. A., Veness, J., Bellemare, M. G., Graves, A., Riedmiller, M., Fidjeland, A. K., Ostrovski, G., Petersen, S., Beattie, C., Sadik, A., Antonoglou, I., King, H., Kumaran, D., Wierstra, D., Legg, S., & Hassabis, D. (2015). Human-level control through deep reinforcement learning. Nature, 518(7540), 529–533.
4. Schrittwieser, J., Antonoglou, I., Hubert, T., Simonyan, K., Sifre, L., Schmitt, S., Guez, A., Lockhart, E., Hassabis, D., Graepel, T., Lillicrap, T., & Silver, D. (2020). Mastering Atari, Go, chess and shogi by planning with a learned model. Nature, 588(7839), 604–609.
5. Hafner, D., Lillicrap, T., Fischer, I., Villegas, R., Ha, D., Lee, H., & Davidson, J. (2019). Learning latent dynamics for planning from pixels. In Proceedings of the 36th International Conference on Machine Learning (ICML).
6. Micheli, V., Alonso, E., & Fleuret, F. (2023). Transformers are sample-efficient world models. In The Eleventh International Conference on Learning Representations (ICLR).
7. Anderson, P., Wu, Q., Teney, D., Bruce, J., Johnson, M., Sünderhauf, N., Reid, I., Gould, S., & van den Hengel, A. (2018). Vision-and-language navigation: Interpreting visually-grounded navigation instructions in real environments. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
8. Shridhar, M., Thomason, J., Gordon, D., Bisk, Y., Han, W., Mottaghi, R., Zettlemoyer, L., & Fox, D. (2020). ALFRED: A benchmark for interpreting grounded instructions for everyday tasks. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
9. Brohan, A., Brown, N., Carbajal, J., Chebotar, Y., Chen, X., Choromanski, K., Ding, T., Driess, D., Dubey, A., Finn, C., Florence, P., Fu, C., Arenas, G., Gopalakrishnan, K., Han, K., Hausman, K., Herzog, A., Hsu, J., Ichter, B., Irpan, A., Joshi, N., Julian, R., Kalashnikov, D., Kuang, Y., Leal, I., Lee, L., Lee, T.-W. E., Levine, S., Lu, Y., Michalewski, H., Mordatch, I., Pertsch, K., Rao, K., Reymann, K., Ryoo, M. S., Salazar, G., Sanketi, P., Sermanet, P., Singh, A., Singh, A., Soricut, R., Tran, H., Vanhoucke, V., Vuong, Q., Wahid, A., Welker, S., Wohlhart, P., Xiao, T., Yu, T., Zitkovich, B., Xu, Z., & Zitkovich, B. (2023). RT-2: Vision-language-action models transfer web knowledge to robotic control. arXiv preprint arXiv:2307.15818.
10. Xiong, Zhexiao, et al. "ActWorld: From Explorable to Interactive World Model via Action-Aware Memory." arXiv preprint arXiv:2606.17730 (2026).
11. Reed, S., Zolna, K., Parisotto, E., Colmenarejo, S. G., Novikov, A., Barth-Maron, G., Giménez, 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., & de Freitas, N. (2022). A generalist agent. Transactions on Machine Learning Research.
12. 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., 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., Niknami, N., Olsson, C., Oren, J., Pal, A., Parekh, B., Pitis, S., Press, O., Qi, P., Raffel, C., Rajpurkar, P., Rao, R., Ré, C., Re, C., Roberts, A., Rodriguez, D., Rosman, J., Sala, F., Schoenick, S., Sontag, D., Srivastava, A., Suhr, A., Szlam, A., Tamkin, A., Tenenbaum, J., Tran, B., Varshney, L. R., Voss, C., Wang, A., Wang, F., Wang, P., Wu, J., Wu, S., Xie, S. M., Xu, F., Yang, S., Yao, Y., Ye, H., Ying, C., Yu, F., Yuksekgonul, M., Zhang, X., Zheng, L., Zhou, K., Zitkovich, B., & Liang, P. (2021). On the opportunities and risks of foundation models. arXiv preprint arXiv:2108.07258.
13. Amodei, D., Olah, C., Steinhardt, J., Christiano, P., Schulman, J., & Mané, D. (2016). Concrete problems in AI safety. arXiv preprint arXiv:1606.06565.
14. Sculley, D., Holt, G., Golovin, D., Davydov, E., Phillips, T., Ebner, D., Chaudhary, V., Young, M., Crespo, J.-F., & Dennison, D. (2015). Hidden technical debt in machine learning systems. In Advances in Neural Information Processing Systems 28 (NeurIPS).
15. Seshia, S. A., Sadigh, D., & Sastry, S. S. (2022). Toward verified artificial intelligence. Communications of the ACM, 65(7), 46–55.
16. Kendall, A., & Gal, Y. (2017). What uncertainties do we need in Bayesian deep learning for computer vision? In Advances in Neural Information Processing Systems 30 (NeurIPS).
17. Parisi, G. I., Kemker, R., Part, J. L., Kanan, C., & Wermter, S. (2019). Continual lifelong learning with neural networks: A review. Neural Networks, 113, 54–71.
18. Mittelstadt, B. D., Allo, P., Taddeo, M., Wachter, S., & Floridi, L. (2016). The ethics of algorithms: Mapping the debate. Big Data & Society, 3(2), 2053951716679679.
19. Suresh, H., & Guttag, J. V. (2021). A framework for understanding sources of harm throughout the machine learning life cycle. In Proceedings of the 1st ACM Conference on Equity and Access in Algorithms, Mechanisms, and Optimization (EAAMO).
20. Raji, I. D., Smart, A., White, R. N., Mitchell, M., Gebru, T., Hutchinson, B., Smith-Loud, J., Theron, D., & Barnes, P. (2020). Closing the AI accountability gap: Defining an end-to-end framework for internal algorithmic auditing. In Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency (FAccT).
21. National Institute of Standards and Technology. (2023). Artificial intelligence risk management framework (AI RMF 1.0). NIST. https://doi.org/10.6028/NIST.AI.100-1
22. Strubell, E., Ganesh, A., & McCallum, A. (2019). Energy and policy considerations for deep learning in NLP. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (ACL).
23. Patterson, D., Gonzalez, J., Le, Q., Liang, C., Munguia, L.-M., Rothchild, D., So, D., Texier, M., & Dean, J. (2021). Carbon emissions and large neural network training. arXiv preprint arXiv:2104.10350.
24. Kiran, B. R., Sobh, I., Talpaert, V., Mannion, P., Al Sallab, A. A., Yogamani, S., & Pérez, P. (2022). Deep reinforcement learning for autonomous driving: A survey. IEEE Transactions on Intelligent Transportation Systems, 23(6), 4909–4926.
25. Ibarz, J., Tan, J., Finn, C., Kalakrishnan, M., Pastor, P., & Levine, S. (2021). How to train your robot with deep reinforcement learning: Lessons we have learned. International Journal of Robotics Research, 40(4-5), 698–721.
Downloads
Published
Issue
Section
License
Copyright (c) 2026 Journal of Advanced Artificial Intelligence Research
This work is licensed under a Creative Commons Attribution 4.0 International License.
This article is published under the Creative Commons Attribution 4.0 International License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
