{"id":1874,"date":"2025-02-24T11:14:58","date_gmt":"2025-02-24T11:14:58","guid":{"rendered":"https:\/\/www.toshiba.eu\/cambridge-research-laboratory\/?page_id=1874"},"modified":"2025-05-06T15:47:31","modified_gmt":"2025-05-06T15:47:31","slug":"world-models-for-embodied-agents","status":"publish","type":"page","link":"https:\/\/www.toshiba.eu\/cambridge-research-laboratory\/embodied-ai\/vision-learning-group\/vlg-resources\/world-models-for-embodied-agents\/","title":{"rendered":"World Models for Embodied Agents"},"content":{"rendered":"\t\t<div data-elementor-type=\"wp-page\" data-elementor-id=\"1874\" class=\"elementor elementor-1874\" data-elementor-post-type=\"page\">\n\t\t\t\t<div class=\"elementor-element elementor-element-5030211 inner-hero mr-0 width-ih e-flex e-con-boxed e-con e-parent\" data-id=\"5030211\" data-element_type=\"container\" data-e-type=\"container\" data-settings=\"{&quot;background_background&quot;:&quot;classic&quot;}\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-b0ce2e1 font-48 elementor-widget elementor-widget-heading\" data-id=\"b0ce2e1\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h1 class=\"elementor-heading-title elementor-size-default\">World Models for Embodied Agents<\/h1>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-368e644 bd-row home mr-0 e-flex e-con-boxed e-con e-parent\" data-id=\"368e644\" data-element_type=\"container\" data-e-type=\"container\" data-settings=\"{&quot;background_background&quot;:&quot;classic&quot;}\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-418573a elementor-icon-list--layout-inline elementor-align-start bd-nav elementor-list-item-link-full_width elementor-widget elementor-widget-icon-list\" data-id=\"418573a\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"icon-list.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<ul class=\"elementor-icon-list-items elementor-inline-items\">\n\t\t\t\t\t\t\t<li class=\"elementor-icon-list-item elementor-inline-item\">\n\t\t\t\t\t\t\t\t\t\t\t<a href=\"https:\/\/www.toshiba.eu\/cambridge-research-laboratory\">\n\n\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"elementor-icon-list-icon\">\n\t\t\t\t\t\t\t<svg xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"16\" height=\"15\" viewBox=\"0 0 16 15\" fill=\"none\"><path d=\"M15.1962 7.48458C15.1962 7.99073 14.8211 8.38722 14.396 8.38722H13.5958L13.6133 12.892C13.6133 12.9679 13.6083 13.0438 13.6008 13.1197V13.5753C13.6008 14.1967 13.1532 14.7 12.6005 14.7H12.2004C12.1729 14.7 12.1454 14.7 12.1179 14.6972C12.0829 14.7 12.0479 14.7 12.0129 14.7L11.2002 14.6972H10.6C10.0474 14.6972 9.59976 14.1939 9.59976 13.5725V11.0979C9.59976 10.6002 9.24217 10.1981 8.79956 10.1981H7.19914C6.75653 10.1981 6.39894 10.6002 6.39894 11.0979V13.5725C6.39894 14.1939 5.95132 14.6972 5.39868 14.6972H4.00082C3.96331 14.6972 3.9258 14.6944 3.88829 14.6916C3.85828 14.6944 3.82827 14.6972 3.79826 14.6972H3.39816C2.84552 14.6972 2.3979 14.1939 2.3979 13.5725V10.4231C2.3979 10.3978 2.3979 10.3696 2.4004 10.3443V8.38441H1.60019C1.15008 8.38441 0.799988 7.99073 0.799988 7.48177C0.799988 7.2287 0.875007 7.00374 1.05005 6.8069L7.45921 0.525005C7.63425 0.328168 7.83431 0.300049 8.00935 0.300049C8.1844 0.300049 8.38445 0.356288 8.53449 0.496885L14.9211 6.80971C15.1212 7.00655 15.2212 7.23151 15.1962 7.48458Z\" fill=\"#007BFF\"><\/path><\/svg>\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t<span class=\"elementor-icon-list-text\">Home<\/span>\n\t\t\t\t\t\t\t\t\t\t\t<\/a>\n\t\t\t\t\t\t\t\t\t<\/li>\n\t\t\t\t\t\t\t\t<li class=\"elementor-icon-list-item elementor-inline-item\">\n\t\t\t\t\t\t\t\t\t\t\t<a href=\"https:\/\/www.toshiba.eu\/cambridge-research-laboratory\">\n\n\t\t\t\t\t\t\t\t\t\t\t<span class=\"elementor-icon-list-text\">CRL<\/span>\n\t\t\t\t\t\t\t\t\t\t\t<\/a>\n\t\t\t\t\t\t\t\t\t<\/li>\n\t\t\t\t\t\t\t\t<li class=\"elementor-icon-list-item elementor-inline-item\">\n\t\t\t\t\t\t\t\t\t\t\t<a href=\"https:\/\/www.toshiba.eu\/cambridge-research-laboratory\/embodied-ai\/\">\n\n\t\t\t\t\t\t\t\t\t\t\t<span class=\"elementor-icon-list-text\">Embodied AI<\/span>\n\t\t\t\t\t\t\t\t\t\t\t<\/a>\n\t\t\t\t\t\t\t\t\t<\/li>\n\t\t\t\t\t\t\t\t<li class=\"elementor-icon-list-item elementor-inline-item\">\n\t\t\t\t\t\t\t\t\t\t\t<a href=\"https:\/\/www.toshiba.eu\/cambridge-research-laboratory\/embodied-ai\/vision-learning-group\/\">\n\n\t\t\t\t\t\t\t\t\t\t\t<span class=\"elementor-icon-list-text\">Vision &amp; Learning<\/span>\n\t\t\t\t\t\t\t\t\t\t\t<\/a>\n\t\t\t\t\t\t\t\t\t<\/li>\n\t\t\t\t\t\t\t\t<li class=\"elementor-icon-list-item elementor-inline-item\">\n\t\t\t\t\t\t\t\t\t\t\t<a href=\"https:\/\/www.toshiba.eu\/cambridge-research-laboratory\/embodied-ai\/vision-learning-group\/vlg-resources\/\">\n\n\t\t\t\t\t\t\t\t\t\t\t<span class=\"elementor-icon-list-text\">VLG Resources\u00a0<\/span>\n\t\t\t\t\t\t\t\t\t\t\t<\/a>\n\t\t\t\t\t\t\t\t\t<\/li>\n\t\t\t\t\t\t\t\t<li class=\"elementor-icon-list-item elementor-inline-item\">\n\t\t\t\t\t\t\t\t\t\t<span class=\"elementor-icon-list-text\">World Models for Embodied Agents<\/span>\n\t\t\t\t\t\t\t\t\t<\/li>\n\t\t\t\t\t\t<\/ul>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-4d3c234 wmea-intro pt-82 pb-82 container-1746 e-flex e-con-boxed e-con e-parent\" data-id=\"4d3c234\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t<div class=\"elementor-element elementor-element-51b8da6 e-grid e-con-full e-con e-child\" data-id=\"51b8da6\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t<div class=\"elementor-element elementor-element-e051a19 e-con-full qig-card e-flex e-con e-child\" data-id=\"e051a19\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t<div class=\"elementor-element elementor-element-a8a3c2f e-con-full card-head e-flex e-con e-child\" data-id=\"a8a3c2f\" data-element_type=\"container\" data-e-type=\"container\" data-settings=\"{&quot;background_background&quot;:&quot;classic&quot;}\">\n\t\t\t\t<div class=\"elementor-element elementor-element-23d9f51 card-img img-multply elementor-widget elementor-widget-image\" data-id=\"23d9f51\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<img fetchpriority=\"high\" decoding=\"async\" width=\"565\" height=\"305\" src=\"https:\/\/www.toshiba.eu\/cambridge-research-laboratory\/wp-content\/uploads\/2025\/02\/wema-img-01-1.png\" class=\"attachment-full size-full wp-image-1897\" alt=\"ReCoRe: Regularized Contrastive Representation Learning of World Model\" srcset=\"https:\/\/www.toshiba.eu\/cambridge-research-laboratory\/wp-content\/uploads\/2025\/02\/wema-img-01-1.png 565w, https:\/\/www.toshiba.eu\/cambridge-research-laboratory\/wp-content\/uploads\/2025\/02\/wema-img-01-1-300x162.png 300w\" sizes=\"(max-width: 565px) 100vw, 565px\" \/>\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-3adafc5 e-con-full card-body e-flex e-con e-child\" data-id=\"3adafc5\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t\t\t<div class=\"elementor-element elementor-element-c54b0c8 font-18 elementor-widget elementor-widget-heading\" data-id=\"c54b0c8\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h4 class=\"elementor-heading-title elementor-size-default\">ReCoRe: Regularized Contrastive Representation Learning of World Model<\/h4>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-58e6a14 font-16 pt-8 pb-30 txt-gap-26 elementor-widget elementor-widget-text-editor\" data-id=\"58e6a14\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p>While recent model-free Reinforcement Learning (RL) methods have demonstrated human-level effectiveness in gaming environments, their success in everyday tasks like visual navigation has been limited, particularly under significant appearance variations. This limitation arises from (i) poor sample efficiency and (ii) over-fitting to training scenarios. To address these challenges, we present a world model that learns invariant features using (i) contrastive unsupervised learning and (ii) an intervention-invariant regularizer. Learning an explicit representation of the world dynamics i.e. a world model, improves sample efficiency while contrastive learning implicitly enforces learning of invariant features, which improves generalization. However, the naive integration of contrastive loss to world models is not good enough, as world-model-based RL methods independently optimize representation learning and agent policy. To overcome this issue, we propose an intervention-invariant regularizer in the form of an auxiliary task such as depth prediction, image denoising, image segmentation, etc., that explicitly enforces invariance to style interventions. Our method outperforms current state-of-the-art model-based and model-free RL methods and significantly improves on out-of-distribution point navigation tasks evaluated on the iGibson benchmark. With only visual observations, we further demonstrate that our approach outperforms recent language-guided foundation models for point navigation, which is essential for deployment on robots with limited computation capabilities. Finally, we demonstrate that our proposed model excels at the sim-to-real transfer of its perception module on the Gibson benchmark.<\/p><p><strong>ReCoRe: Regularized Contrastive Representation Learning of World Model<\/strong>\u2028Rudra P.K. Poudel, Harit Pandya, Stephan Liwicki, Roberto Cipolla \/ CVPR 2024 \/\u00a0<a href=\"https:\/\/arxiv.org\/pdf\/2312.09056\" target=\"_blank\" rel=\"nofollow noopener sponsored\">arXiv<\/a><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-4b29fea e-con-full card-footer e-flex e-con e-child\" data-id=\"4b29fea\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t\t\t<div class=\"elementor-element elementor-element-79f6bda elementor-align-left btn-download-red elementor-widget elementor-widget-button\" data-id=\"79f6bda\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"button.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<div class=\"elementor-button-wrapper\">\n\t\t\t\t\t<a class=\"elementor-button elementor-button-link elementor-size-sm\" href=\"#\">\n\t\t\t\t\t\t<span class=\"elementor-button-content-wrapper\">\n\t\t\t\t\t\t<span class=\"elementor-button-icon\">\n\t\t\t\t<svg xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"18\" height=\"18\" viewBox=\"0 0 18 18\" fill=\"none\"><path d=\"M17.5418 11.9512C17.2888 11.9512 17.0835 12.1564 17.0835 12.4094V14.1603C17.0835 15.171 16.2613 15.9932 15.2507 15.9932H2.74932C1.73869 15.9932 0.916453 15.171 0.916453 14.1603V12.4094C0.916453 12.1564 0.711316 11.9512 0.458227 11.9512C0.205137 11.9512 0 12.1563 0 12.4094V14.1603C0 15.6763 1.23335 16.9096 2.74932 16.9096H15.2507C16.7666 16.9096 18 15.6763 18 14.1603V12.4094C18 12.1563 17.7949 11.9512 17.5418 11.9512Z\" fill=\"#0064D2\"><\/path><path d=\"M14.759 13.7566H3.23948C2.98646 13.7566 2.78125 13.9618 2.78125 14.2148C2.78125 14.4678 2.98639 14.673 3.23948 14.673H14.7589C15.012 14.673 15.2172 14.4679 15.2172 14.2148C15.2172 13.9618 15.012 13.7566 14.759 13.7566Z\" fill=\"#0064D2\"><\/path><path d=\"M13.6382 7.31003C13.4696 7.12135 13.18 7.10493 12.9912 7.27354L9.4586 10.4293V1.54856C9.4586 1.29554 9.25347 1.09033 9.00038 1.09033C8.74736 1.09033 8.54215 1.29547 8.54215 1.54856V10.4293L5.00958 7.27358C4.8209 7.10497 4.53121 7.12142 4.36257 7.31007C4.19403 7.49882 4.2103 7.78851 4.39902 7.95709L8.23717 11.3858C8.45468 11.5801 8.72746 11.6773 9.00034 11.6773C9.27312 11.6773 9.54593 11.5801 9.76351 11.3859L13.6017 7.95709C13.7905 7.78841 13.8068 7.49872 13.6382 7.31003Z\" fill=\"#0064D2\"><\/path><\/svg>\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t<span class=\"elementor-button-text\">Download Code<\/span>\n\t\t\t\t\t<\/span>\n\t\t\t\t\t<\/a>\n\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-607d624 btn-video elementor-widget elementor-widget-exad-modal-popup\" data-id=\"607d624\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"exad-modal-popup.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\r\n\t\t<div class=\"exad-modal\">\r\n\t\t\t<div class=\"exad-modal-wrapper\">\r\n\r\n\t\t\t\t<div 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class=\"exad-modal-content\">\r\n\t\t\t\t\t\t<div class=\"exad-modal-element \">\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<video class=\"exad-video-hosted\" src=\"https:\/\/www.toshiba.eu\/cambridge-research-laboratory\/wp-content\/uploads\/2025\/02\/ReCoRe-Presentation-CVPR-2024.mp4\" controls=\"\" controlslist=\"nodownload\">\r\n\t\t\t\t\t\t\t\t<\/video>\r\n\t\t\t\t\t\t\t\r\n\t\t\t\t\t\t\t<div class=\"exad-close-btn\">\r\n\t\t\t\t\t\t\t\t<span><\/span>\r\n\t\t\t\t\t\t\t<\/div>\r\n\r\n\t\t\t\t\t\t<\/div>\r\n\t\t\t\t\t<\/div>\r\n\t\t\t\t<\/div>\r\n\t\t\t<\/div>\r\n\t\t\t<div class=\"exad-modal-overlay\" data-exad_overlay_click_close=\"yes\"><\/div>\r\n\t\t<\/div>\r\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-3ceec06 e-con-full qig-card e-flex e-con e-child\" data-id=\"3ceec06\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t<div class=\"elementor-element elementor-element-75380ed e-con-full card-head e-flex e-con e-child\" data-id=\"75380ed\" data-element_type=\"container\" data-e-type=\"container\" data-settings=\"{&quot;background_background&quot;:&quot;classic&quot;}\">\n\t\t\t\t<div class=\"elementor-element elementor-element-f190317 card-img img-multply elementor-widget elementor-widget-image\" data-id=\"f190317\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<img decoding=\"async\" width=\"565\" height=\"305\" src=\"https:\/\/www.toshiba.eu\/cambridge-research-laboratory\/wp-content\/uploads\/2025\/02\/wema-img-02.png\" class=\"attachment-full size-full wp-image-1965\" alt=\"LanGWM: Language Grounded World Model\" srcset=\"https:\/\/www.toshiba.eu\/cambridge-research-laboratory\/wp-content\/uploads\/2025\/02\/wema-img-02.png 565w, https:\/\/www.toshiba.eu\/cambridge-research-laboratory\/wp-content\/uploads\/2025\/02\/wema-img-02-300x162.png 300w\" sizes=\"(max-width: 565px) 100vw, 565px\" \/>\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-a1848e4 e-con-full card-body e-flex e-con e-child\" data-id=\"a1848e4\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t\t\t<div class=\"elementor-element elementor-element-79eba85 font-18 elementor-widget elementor-widget-heading\" data-id=\"79eba85\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h4 class=\"elementor-heading-title elementor-size-default\">LanGWM: Language Grounded World Model<\/h4>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-2cc67fe font-16 pt-8 pb-30 txt-gap-26 elementor-widget elementor-widget-text-editor\" data-id=\"2cc67fe\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p>Recent advances in deep reinforcement learning have showcased its potential in tackling complex tasks. However, experiments on visual control tasks have revealed that state-of-the-art reinforcement learning models struggle with out-of-distribution generalization. Conversely, expressing higher-level concepts and global contexts is relatively easy using language. Building upon the recent success of the large language models, our main objective is to improve the state abstraction technique in reinforcement learning by leveraging language for robust action selection. Specifically, we focus on learning language grounded visual features to enhance the world model learning, a model-based reinforcement learning technique. To enforce our hypothesis explicitly, we mask out the bounding boxes of a few objects in the image observation and provide the text prompt as descriptions for these masked objects. Subsequently, we predict the masked objects and surrounding regions as pixel reconstruction, similar to the transformer-based masked autoencoder approach. Our proposed LanGWM: Language Grounded World Model achieves state-of-the-art performance in out-of-distribution test at the 100K interaction steps benchmarks of iGibson point navigation tasks. Furthermore, our proposed technique of explicit language grounded visual representation learning has the potential to improve models for human-robot interaction because our extracted visual features are language grounded.<\/p><p><strong>LanGWM: Language Grounded World Model<\/strong><br \/>Rudra P.K. Poudel, Harit Pandya, Chao Zhang, Roberto Cipolla \/\u00a0<a href=\"https:\/\/arxiv.org\/pdf\/2311.17593\" target=\"_blank\" rel=\"nofollow noopener sponsored\">arXiv<\/a><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-dd1ad82 e-con-full qig-card e-flex e-con e-child\" data-id=\"dd1ad82\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t<div class=\"elementor-element elementor-element-3e6e042 e-con-full card-head e-flex e-con e-child\" data-id=\"3e6e042\" data-element_type=\"container\" data-e-type=\"container\" data-settings=\"{&quot;background_background&quot;:&quot;classic&quot;}\">\n\t\t\t\t<div class=\"elementor-element elementor-element-991f50f card-img img-multply elementor-widget elementor-widget-image\" data-id=\"991f50f\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<img decoding=\"async\" width=\"565\" height=\"305\" src=\"https:\/\/www.toshiba.eu\/cambridge-research-laboratory\/wp-content\/uploads\/2025\/02\/wema-img-03.png\" class=\"attachment-full size-full wp-image-1966\" alt=\"Contrastive Unsupervised Learning of World Model with Invariant Causal Features\" srcset=\"https:\/\/www.toshiba.eu\/cambridge-research-laboratory\/wp-content\/uploads\/2025\/02\/wema-img-03.png 565w, https:\/\/www.toshiba.eu\/cambridge-research-laboratory\/wp-content\/uploads\/2025\/02\/wema-img-03-300x162.png 300w\" sizes=\"(max-width: 565px) 100vw, 565px\" \/>\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-cc7fafa font-13 mtn-57 elementor-widget elementor-widget-text-editor\" data-id=\"cc7fafa\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p>Observation is made of content (C), causal variables, and style (S), spurious variables. We want representation learning to extract the content variables only, i.e. true cause of the action.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-2e10951 e-con-full card-body e-flex e-con e-child\" data-id=\"2e10951\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t\t\t<div class=\"elementor-element elementor-element-1085f94 font-18 elementor-widget elementor-widget-heading\" data-id=\"1085f94\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h4 class=\"elementor-heading-title elementor-size-default\">Contrastive Unsupervised Learning of World Model with Invariant Causal Features<\/h4>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-f47fbd9 font-16 pt-8 pb-30 txt-gap-26 elementor-widget elementor-widget-text-editor\" data-id=\"f47fbd9\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p>In this paper we present a world model, which learns causal features using the invariance principle. In particular, we use contrastive unsupervised learning to learn the invariant causal features, which enforces invariance across augmentations of irrelevant parts or styles of the observation. The world-model-based reinforcement learning methods independently optimize representation learning and the policy. Thus, naive contrastive loss implementation collapses due to a lack of supervisory signals to the representation learning module. We propose an intervention invariant auxiliary task to mitigate this issue. Specifically, we use data augmentation as style intervention on the RGB observation space and depth prediction as an auxiliary task to explicitly enforce the invariance. Our proposed method significantly outperforms current state-of-the-art model-based and model-free reinforcement learning methods on out-of-distribution point navigation tasks on the iGibson dataset. Moreover, our proposed model excels at the sim-to-real transfer of our perception learning module. Finally, we evaluate our approach on the DeepMind control suite and enforce invariance only implicitly since depth is not available. Nevertheless, our proposed model performs on par with the state-of-the-art counterpart.<\/p><p><strong>Contrastive Unsupervised Learning of World Model with Invariant Causal Features<\/strong><br \/>Rudra P.K. Poudel, Harit Pandya, Roberto Cipolla \/ NeurIPS Workshop 2022 \/\u00a0<a href=\"https:\/\/arxiv.org\/pdf\/2209.14932\" target=\"_blank\" rel=\"nofollow noopener sponsored\">arXiv<\/a><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-349f6f6 e-con-full botton-row pt-51 e-flex e-con e-child\" data-id=\"349f6f6\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t\t\t<div class=\"elementor-element elementor-element-c28991c elementor-align-left btn-bb-red elementor-widget elementor-widget-button\" data-id=\"c28991c\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"button.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<div class=\"elementor-button-wrapper\">\n\t\t\t\t\t<a class=\"elementor-button elementor-button-link elementor-size-sm\" 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url('https:\/\/www.toshiba.eu\/cambridge-research-laboratory\/wp-content\/uploads\/2025\/02\/ToshibaSans-Regular.ttf') format('truetype');\n}\n@font-face {\n\tfont-family: 'Toshiba Sans';\n\tfont-style: normal;\n\tfont-weight: 500;\n\tfont-display: auto;\n\tsrc: url('https:\/\/www.toshiba.eu\/cambridge-research-laboratory\/wp-content\/uploads\/2025\/02\/ToshibaSans-Medium.ttf') format('truetype');\n}\n@font-face {\n\tfont-family: 'Toshiba Sans';\n\tfont-style: normal;\n\tfont-weight: bold;\n\tfont-display: auto;\n\tsrc: url('https:\/\/www.toshiba.eu\/cambridge-research-laboratory\/wp-content\/uploads\/2025\/02\/ToshibaSans-Bold.ttf') format('truetype');\n}\n\/* End Custom Fonts CSS *\/<\/style>\t\t<div data-elementor-type=\"loop-item\" data-elementor-id=\"5261\" class=\"elementor elementor-5261 e-loop-item e-loop-item-8167 post-8167 latest-publication type-latest-publication status-publish hentry publications_category-embodied-ai publications_category-vision-learning-group ast-article-single\" data-elementor-post-type=\"elementor_library\" data-custom-edit-handle=\"1\">\n\t\t\t<div data-pp-template-widget-id=\"ee29110-7726\" class=\"elementor-pp-element-ee29110-7726 elementor-element elementor-element-ee29110 e-con-full pub-content pb-34 e-flex e-con e-parent\" data-id=\"ee29110\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t\t\t<div data-pp-template-widget-id=\"f9ed225-8167\" class=\"elementor-pp-element-f9ed225-8167 elementor-element elementor-element-f9ed225 font-15 elementor-widget elementor-widget-text-editor\" data-id=\"f9ed225\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t2025\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div data-pp-template-widget-id=\"b29bdd0-8167\" class=\"elementor-pp-element-b29bdd0-8167 elementor-element elementor-element-b29bdd0 font-25 pt-19 pb-9 elementor-widget elementor-widget-theme-post-title elementor-page-title elementor-widget-heading\" data-id=\"b29bdd0\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"theme-post-title.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h4 class=\"elementor-heading-title elementor-size-default\"><a href=\"https:\/\/openaccess.thecvf.com\/content\/WACV2025\/papers\/Logothetis_NPL-MVPS_Neural_Point-Light_Multi-View_Photometric_Stereo_WACV_2025_paper.pdf\" target=\"_blank\" rel=\"noopener\">NPLMV-PS: Neural Point-Light Multi-View Photometric Stereo<\/a><\/h4>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div data-pp-template-widget-id=\"9c3f25e-8167\" class=\"elementor-pp-element-9c3f25e-8167 elementor-element elementor-element-9c3f25e font-15 elementor-widget elementor-widget-text-editor\" data-id=\"9c3f25e\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p>F Logothetis, I Budvytis, R Cipolla<\/p>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div data-pp-template-widget-id=\"405d361-8167\" class=\"elementor-pp-element-405d361-8167 elementor-element elementor-element-405d361 font-15 pb-36 elementor-widget elementor-widget-text-editor\" data-id=\"405d361\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\tWACV 2025\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div data-pp-template-widget-id=\"c67fb5d-8167\" class=\"elementor-pp-element-c67fb5d-8167 elementor-element elementor-element-c67fb5d elementor-align-left btn-rm elementor-widget elementor-widget-button\" data-id=\"c67fb5d\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"button.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<div class=\"elementor-button-wrapper\">\n\t\t\t\t\t<a class=\"elementor-button elementor-button-link elementor-size-sm\" href=\"https:\/\/openaccess.thecvf.com\/content\/WACV2025\/papers\/Logothetis_NPL-MVPS_Neural_Point-Light_Multi-View_Photometric_Stereo_WACV_2025_paper.pdf\" rel=\"noopener\">\n\t\t\t\t\t\t<span class=\"elementor-button-content-wrapper\">\n\t\t\t\t\t\t<span class=\"elementor-button-icon\">\n\t\t\t\t<svg xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"15\" height=\"15\" viewBox=\"0 0 15 15\" fill=\"none\"><rect class=\"icon-circle\" x=\"0.5\" y=\"0.5\" width=\"14\" height=\"14\" rx=\"7\" stroke=\"#C5C5C5\"><\/rect><path class=\"icon-arrow\" d=\"M9.54496 7.85355C9.74022 7.65829 9.74022 7.34171 9.54496 7.14645L6.36298 3.96447C6.16772 3.7692 5.85113 3.7692 5.65587 3.96447C5.46061 4.15973 5.46061 4.47631 5.65587 4.67157L8.4843 7.5L5.65587 10.3284C5.46061 10.5237 5.46061 10.8403 5.65587 11.0355C5.85113 11.2308 6.16772 11.2308 6.36298 11.0355L9.54496 7.85355ZM8.19141 8H9.19141V7H8.19141V8Z\" fill=\"black\"><\/path><\/svg>\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t<span class=\"elementor-button-text\">Read More<\/span>\n\t\t\t\t\t<\/span>\n\t\t\t\t\t<\/a>\n\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<\/div>\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<div class=\"pp-post-wrap pp-grid-item-wrap elementor-grid-item post-8168 latest-publication type-latest-publication status-publish hentry publications_category-embodied-ai publications_category-vision-learning-group\">\n\t\t\t\t\t\t<div class=\"pp-post pp-grid-item\">\n\t\t\t\t<div class=\"elementor-template\"><style>.elementor-5261 .elementor-element.elementor-element-ee29110{--display:flex;--flex-direction:column;--container-widget-width:100%;--container-widget-height:initial;--container-widget-flex-grow:0;--container-widget-align-self:initial;--flex-wrap-mobile:wrap;--gap:0px 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.pp-tooltip-content{font-size:var( --e-global-typography-accent-font-size );line-height:var( --e-global-typography-accent-line-height );}.elementor-widget-theme-post-title .elementor-heading-title{font-size:var( --e-global-typography-primary-font-size );line-height:var( --e-global-typography-primary-line-height );}.elementor-widget-button .elementor-button{font-size:var( --e-global-typography-accent-font-size );line-height:var( --e-global-typography-accent-line-height );}}@media(min-width:2400px){.elementor-widget-text-editor{font-size:var( --e-global-typography-text-font-size );line-height:var( --e-global-typography-text-line-height );}.pp-tooltip.pp-tooltip-{{ID}} .pp-tooltip-content{font-size:var( --e-global-typography-accent-font-size );line-height:var( --e-global-typography-accent-line-height );}.elementor-widget-theme-post-title .elementor-heading-title{font-size:var( --e-global-typography-primary-font-size );line-height:var( --e-global-typography-primary-line-height 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{\n\tfont-family: 'Toshiba Sans';\n\tfont-style: normal;\n\tfont-weight: bold;\n\tfont-display: auto;\n\tsrc: url('https:\/\/www.toshiba.eu\/cambridge-research-laboratory\/wp-content\/uploads\/2025\/02\/ToshibaSans-Bold.ttf') format('truetype');\n}\n\/* End Custom Fonts CSS *\/<\/style>\t\t<div data-elementor-type=\"loop-item\" data-elementor-id=\"5261\" class=\"elementor elementor-5261 e-loop-item e-loop-item-8168 post-8168 latest-publication type-latest-publication status-publish hentry publications_category-embodied-ai publications_category-vision-learning-group ast-article-single\" data-elementor-post-type=\"elementor_library\" data-custom-edit-handle=\"1\">\n\t\t\t<div data-pp-template-widget-id=\"ee29110-7726\" class=\"elementor-pp-element-ee29110-7726 elementor-element elementor-element-ee29110 e-con-full pub-content pb-34 e-flex e-con e-parent\" data-id=\"ee29110\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t\t\t<div data-pp-template-widget-id=\"f9ed225-8168\" class=\"elementor-pp-element-f9ed225-8168 elementor-element elementor-element-f9ed225 font-15 elementor-widget elementor-widget-text-editor\" data-id=\"f9ed225\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t2025\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div data-pp-template-widget-id=\"b29bdd0-8168\" class=\"elementor-pp-element-b29bdd0-8168 elementor-element elementor-element-b29bdd0 font-25 pt-19 pb-9 elementor-widget elementor-widget-theme-post-title elementor-page-title elementor-widget-heading\" data-id=\"b29bdd0\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"theme-post-title.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h4 class=\"elementor-heading-title elementor-size-default\"><a href=\"https:\/\/proceedings.neurips.cc\/paper_files\/paper\/2024\/file\/19f7f755908372efb25826d61959cdf9-Paper-Conference.pdf\" target=\"_blank\" rel=\"noopener\">Recurrent Reinforcement Learning with Memoroids<\/a><\/h4>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div data-pp-template-widget-id=\"9c3f25e-8168\" class=\"elementor-pp-element-9c3f25e-8168 elementor-element elementor-element-9c3f25e font-15 elementor-widget elementor-widget-text-editor\" data-id=\"9c3f25e\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p>S Morad, C Lu, R Kortvelesy, S Liwicki, J Foerster, A Prorok<\/p>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div data-pp-template-widget-id=\"405d361-8168\" class=\"elementor-pp-element-405d361-8168 elementor-element elementor-element-405d361 font-15 pb-36 elementor-widget elementor-widget-text-editor\" data-id=\"405d361\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\tNeurIPS 2024\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div data-pp-template-widget-id=\"c67fb5d-8168\" class=\"elementor-pp-element-c67fb5d-8168 elementor-element elementor-element-c67fb5d elementor-align-left btn-rm elementor-widget elementor-widget-button\" data-id=\"c67fb5d\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"button.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<div class=\"elementor-button-wrapper\">\n\t\t\t\t\t<a class=\"elementor-button elementor-button-link elementor-size-sm\" href=\"https:\/\/proceedings.neurips.cc\/paper_files\/paper\/2024\/file\/19f7f755908372efb25826d61959cdf9-Paper-Conference.pdf\" rel=\"noopener\">\n\t\t\t\t\t\t<span class=\"elementor-button-content-wrapper\">\n\t\t\t\t\t\t<span 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elementor-grid-item post-8169 latest-publication type-latest-publication status-publish hentry publications_category-embodied-ai publications_category-vision-learning-group\">\n\t\t\t\t\t\t<div class=\"pp-post pp-grid-item\">\n\t\t\t\t<div class=\"elementor-template\"><style>.elementor-5261 .elementor-element.elementor-element-ee29110{--display:flex;--flex-direction:column;--container-widget-width:100%;--container-widget-height:initial;--container-widget-flex-grow:0;--container-widget-align-self:initial;--flex-wrap-mobile:wrap;--gap:0px 0px;--row-gap:0px;--column-gap:0px;border-style:solid;--border-style:solid;border-width:0px 0px 1px 0px;--border-top-width:0px;--border-right-width:0px;--border-bottom-width:1px;--border-left-width:0px;border-color:#0064D2;--border-color:#0064D2;--border-radius:0px 0px 0px 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url('https:\/\/www.toshiba.eu\/cambridge-research-laboratory\/wp-content\/uploads\/2025\/02\/ToshibaSans-Light.ttf') format('truetype');\n}\n@font-face {\n\tfont-family: 'Toshiba Sans';\n\tfont-style: normal;\n\tfont-weight: 400;\n\tfont-display: auto;\n\tsrc: url('https:\/\/www.toshiba.eu\/cambridge-research-laboratory\/wp-content\/uploads\/2025\/02\/ToshibaSans-Regular.ttf') format('truetype');\n}\n@font-face {\n\tfont-family: 'Toshiba Sans';\n\tfont-style: normal;\n\tfont-weight: 500;\n\tfont-display: auto;\n\tsrc: url('https:\/\/www.toshiba.eu\/cambridge-research-laboratory\/wp-content\/uploads\/2025\/02\/ToshibaSans-Medium.ttf') format('truetype');\n}\n@font-face {\n\tfont-family: 'Toshiba Sans';\n\tfont-style: normal;\n\tfont-weight: bold;\n\tfont-display: auto;\n\tsrc: url('https:\/\/www.toshiba.eu\/cambridge-research-laboratory\/wp-content\/uploads\/2025\/02\/ToshibaSans-Bold.ttf') format('truetype');\n}\n\/* End Custom Fonts CSS *\/<\/style>\t\t<div data-elementor-type=\"loop-item\" data-elementor-id=\"5261\" class=\"elementor elementor-5261 e-loop-item e-loop-item-8169 post-8169 latest-publication type-latest-publication status-publish hentry publications_category-embodied-ai publications_category-vision-learning-group ast-article-single\" data-elementor-post-type=\"elementor_library\" data-custom-edit-handle=\"1\">\n\t\t\t<div data-pp-template-widget-id=\"ee29110-7726\" class=\"elementor-pp-element-ee29110-7726 elementor-element elementor-element-ee29110 e-con-full pub-content pb-34 e-flex e-con e-parent\" data-id=\"ee29110\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t\t\t<div data-pp-template-widget-id=\"f9ed225-8169\" class=\"elementor-pp-element-f9ed225-8169 elementor-element elementor-element-f9ed225 font-15 elementor-widget elementor-widget-text-editor\" data-id=\"f9ed225\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t2024\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div data-pp-template-widget-id=\"b29bdd0-8169\" class=\"elementor-pp-element-b29bdd0-8169 elementor-element elementor-element-b29bdd0 font-25 pt-19 pb-9 elementor-widget elementor-widget-theme-post-title elementor-page-title elementor-widget-heading\" data-id=\"b29bdd0\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"theme-post-title.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h4 class=\"elementor-heading-title elementor-size-default\"><a href=\"https:\/\/openaccess.thecvf.com\/content\/WACV2024\/papers\/Logothetis_A_Neural_Height-Map_Approach_for_the_Binocular_Photometric_Stereo_Problem_WACV_2024_paper.pdf\" target=\"_blank\" rel=\"noopener\">A Neural Height-Map Approach for the Binocular Photometric Stereo Problem<\/a><\/h4>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div data-pp-template-widget-id=\"9c3f25e-8169\" class=\"elementor-pp-element-9c3f25e-8169 elementor-element elementor-element-9c3f25e font-15 elementor-widget elementor-widget-text-editor\" data-id=\"9c3f25e\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p>F Logothetis, I Budvytis, R Cipolla<\/p>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div data-pp-template-widget-id=\"405d361-8169\" class=\"elementor-pp-element-405d361-8169 elementor-element elementor-element-405d361 font-15 pb-36 elementor-widget elementor-widget-text-editor\" data-id=\"405d361\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\tWACV 2024\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div data-pp-template-widget-id=\"c67fb5d-8169\" class=\"elementor-pp-element-c67fb5d-8169 elementor-element elementor-element-c67fb5d 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