{
    "componentChunkName": "component---src-gatsby-entities-page-tsx",
    "path": "/publications/robotics-and-the-challenge-of-embodied-intelligence",
    "result": {"data":{"platform":{"page":{"id":"65c55d169e947c438698b026","slug":"robotics-and-the-challenge-of-embodied-intelligence","path":"/publications/robotics-and-the-challenge-of-embodied-intelligence","name":"Robotics and the Challenge of Embodied Intelligence","title":"Robotics and the Challenge of Embodied Intelligence","pretitle":"Invited Contribution","subtitle":null,"content":{"text":"Despite recent breakthroughs in artificial intelligence (AI), robots’ cognitive systems still lack many of the abilities that humans possess when it comes to tasks such as robust perception, adaptive fine motor control, and adaptation to external conditions. For this reason, robots tend to operate most reliably within precisely calibrated and controlled environments or for a predefined set of operations. To perform in the world at large, they will need to reliably sense and plan their actions depending on, and continuously adapting to, the external environment and their internal state. If this can be achieved, they will be able to interact seamlessly with the environment and other agents to accomplish a range of different tasks, much as humans do. This “embodied” intelligence will represent a genuine breakthrough in creating machines that are able to reason, based on their own perceptions, in order to achieve a desired outcome.\n\nEmbodied intelligence is based on two distinct principles. The first principle is that **the way a body is built is instrumental to its perception of the external world and to its behaviour and actions**. That is, the way a robot is constructed will govern what it can perceive and what it will do: having two eyes, for example, gives depth perception through stereo vision; having legs connected to the body through hips enables the robot to exploit pendulum dynamics, allowing it to walk more energy-efficiently.\n\nThe second principle of embodied intelligence is **the linking of morphology, perception and action**. Current disembodied image recognition systems, based on deep learning, are trained on many online images, typically taken passively by static cameras. These are thus completely uncorrelated with whatever action a robot has done, or needs to take. The problem of perception becomes more difficult still because there is no information about how the object sounds, feels, or changes in appearance when moved. A robot looking at the world, however, can move its sensors and interact with the object to improve what it sees, in the interest of acquiring more information. It might, for example, move it to gain a clearer view, or rotate it to get multiple views; it can also use additional senses, such as touch, to gain further information about the object. It can simultaneously relate its actions to their sensory consequences in order to improve its execution of a specific action or particular function. \n\nFurthermore, the interplay between the morphology of the robot and its behaviour can improve the efficiency of the overall system: for example, foveated vision allows for both high-resolution central vision and a wide field of view, while optimising resources (size, connectivity and data load), provided it is coupled with the intelligence needed to move the eyes towards salient stimuli. \n\nThose are the principles; what about the practice? The datasets currently used for training state of the art AI models are mostly disembodied, and therefore do not allow us to study the role of learning in the interplay between morphology, actions, and perception. Embodiment enhances the possibility of developing intelligent and efficient systems, using less data to learn and supporting continual learning in new environments. So **the first challenge is to understand how to encode intelligence robustly**, enabling data from multiple sources to be processed on multiple timescales to ensure life-long learning, adaptation and memory organisation. \n\nCurrent AI methods also have immense computational resource requirements, requiring large datasets and lengthy training times, and often also expensive hardware and vast amounts of energy. That means they are not well suited to embodied AI, which will have to deal with multiple different scenarios expediently, without learning from a vast new dataset for each situation. So **the second challenge is to develop embodied AI models that can efficiently adapt and learn** new tasks in ever changing scenarios, using less data, time, computational resources and energy. \n\nRecent meta-learning techniques can improve on the problem and lead to systems that can adapt to novel situations: using “one-shot” or “few-shot” learning, a system can assimilate knowledge and learn a new task from just a few examples.  However, this will barely approach the efficiency of biological neural networks shaped by millions of years of evolution. The biological networks are vastly more efficient than conventional computing at perception, hierarchical information extraction, adaptation, continual learning, and memorisation of temporally structured data. \n\nIf we can understand how neurobiology evolved, develops, and works, we can use the same principles to give us a head start in developing **neuromorphic computational systems** for robotics that will show robust, reliable and adaptive performance in complex, ever-changing and uncontrolled environments, as well as higher efficiency, lower latency and reduced computational cost. There are multiple “technological” and computational factors that underlie the brain’s capabilities, including event-driven encoding, high parallelism, and the capability of neurons to compute and store information in the same place. \n\nConsider the first of these: event-driven encoding. Today’s conventional sensors sample and process information at single moments in time, determined by the rhythm of an internal clock, rather than being driven by external events. By contrast, biological systems sense and process *changes* in sensory data, sampling data as and when these changes occur. For example, if we flick a switch so a light goes off, the retina of the eye is stimulated and information about the *change* is transferred to the brain, rather than an ongoing succession of reports about the on or off status of the light. This *event-driven* sensory information is not only very rich, but requires much less computational power than a clock-driven system, and can thus allow the computational sensory system to become much more efficient than conventional AI.\n\nWhile GPUs support a high degree of parallelism, they are still based on conventional computing infrastructure, whereby memory and processing are physically separated: performing an operation requires the machine to extract the relevant information from its memory, transfer it to the processor, perform the operation, and then transfer the outcome back into memory. This comprises most of the cost of current computation. By bringing these two functions together (while also supporting highly parallel computation) as they are in the brain, neuromorphic computing has the potential to vastly increase the efficiency of the computational process itself.\n\nNeuromorphic technology is moving embodied artificial intelligence on from a static, frame-based computing system to a dynamic, event-based system able to efficiently process information and adapt to new situations. While event-driven vision sensors have reached the consumer’s market, neuromorphic computing platforms are still evolving and mostly used within the research domain. In the next few years, we will see silicon-based platforms in the market. At the same time, evolved systems, with additional computational primitives and memristive devices for co-localisation of memory and computing, will be available to the research community at large. Novel neuromorphic computing devices are in the making, based on flexible organic electronics, organoids, photonics, etc.\n\nProgress in neural computation, as well as neuromorphic hardware, will be needed to build neuromorphic systems. Describing a brain’s computational process mathematically, to produce a function that can be plugged into a processing system, is challenging, especially since we don’t yet fully understand all of biological computing’s components and their computational role in a unified conceptual framework. In a biological system, neurons can not only interact with one another, but their computational function is affected by the *manner* of their interaction. There are different forms of plasticity and computation performed at different temporal and spatial scales. \n\nAs an example, while most neuromorphic computing uses point neurons, biological neurons are way more complex: dendrites, the branched, tree-like structures at the end of neurons, gather signals though contacts with other cells in their proximity and from other brain areas. The signal received depends not only on the type of chemical contact between the cells, but also on exactly where the two branches of the cells interact and the time it takes for these signals to travel through them. More neuroscience research is needed to understand the computational relevance of each component of the biological system; this understanding can then be integrated into neuromorphic computing and in the development of new neuromorphic hardware platforms. \n\nThat will make them much more useful in relatively unconstrained real-world situations, as already demonstrated by proof-of-concept systems that can solve specific perception and control tasks, and carry out simple decision-making such as visual and auditory classification, sound source localisation, visual and auditory attention, basic navigation, obstacle avoidance, landmark recognition, trajectory prediction, and so on. In few years, more robustness and adaptation capabilities will be achieved by the integration of new computational methods such as balanced EI networks, dynamic attractors, multisensory integration and lifelong learning. This will later need to be scaled up and integrated with cognitive large scale brain models to solve complex human-like tasks.\n\nThere is a long way to go, but neuromorphic computing to create robots with embodied intelligence is essential for machines to display true hallmarks of cognition, and for artificial intelligence to become meaningfully intelligent. Machines need to be aware of their environment, be self-aware of their own bodies, robustly interpret their own sensory data, and make decisions about the action that will best fulfil their task. Only then will we have robots that are truly fit for the real world."},"titleRich":{"id":"65c55d169e947c438698b026_titleRich","text":""},"subtitleRich":{"id":"65c55d169e947c438698b026_subtitleRich","text":""},"openGraph":{"title":"Robotics and the Challenge of Embodied Intelligence","description":{"plain":"The idea that we will soon have robots capable of performing many of the same tasks as humans has become commonplace, thanks to their depiction in films, television and books. However, the design of robots that can sense and process information about their environment, then use this information to make decisions about their own behaviour, remains a major and multi-faceted research and engineering challenge.\n"},"image":{"url":"https://res.cloudinary.com/shapeable/image/upload/v1696727257/gesda-23/banner/banner-ic-1-3-robotics_thumbnail__Banner_-_IC1-3_Robotics-thumb_lhjh0u.webp","url2x":null,"thumbnails":{"large":{"url":"https://res.cloudinary.com/shapeable/image/upload/c_limit,h_512/v1696727257/gesda-23/banner/banner-ic-1-3-robotics_thumbnail__Banner_-_IC1-3_Robotics-thumb_lhjh0u.webp","url2x":"https://res.cloudinary.com/shapeable/image/upload/c_limit,h_1024/v1696727257/gesda-23/banner/banner-ic-1-3-robotics_thumbnail__Banner_-_IC1-3_Robotics-thumb_lhjh0u.webp"}}}},"parent":{"id":"6a9362ba34307dba8bf55024","name":"Publications","path":"/publications"},"typeLabel":"Invited Contribution","type":{"name":"Invited Contribution","slug":"invited-contribution"},"types":[{"name":"Invited Contribution","slug":"invited-contribution"}],"intro":{"text":"The idea that we will soon have robots capable of performing many of the same tasks as humans has become commonplace, thanks to their depiction in films, television and books. However, the design of robots that can sense and process information about their environment, then use this information to make decisions about their own behaviour, remains a major and multi-faceted research and engineering challenge."},"orderNumber":null,"files":[],"family":[{"id":"65c55d169e947c438698b026","name":"Robotics and the Challenge of Embodied Intelligence","slug":"robotics-and-the-challenge-of-embodied-intelligence","path":"/publications/robotics-and-the-challenge-of-embodied-intelligence","subtitle":null}],"moderators":[],"authors":[{"__typename":"Platform_Person","id":"65c55d249e947c438698b0cc","name":"Chiara Bartolozzi","slug":"chiara-bartolozzi-1","position":"Head of Event-Driven Perception for Robotics Group","path":"/people/chiara-bartoluzzi","photo":{"id":"image_gesda-23/person/chiara-bartolozzi-1_photo__Chiara_Bartolozzi_scg95j","url":"https://res.cloudinary.com/shapeable/image/upload/v1694949819/gesda-23/person/chiara-bartolozzi-1_photo__Chiara_Bartolozzi_scg95j.jpg","url2x":null},"organisation":{"id":"65c55d059e947c438698ac98","name":"Italian Institute for Technology","slug":"italian-institute-for-technology-1"}}],"people":[{"id":"65c55d249e947c438698b0cc","name":"Chiara Bartolozzi","slug":"chiara-bartolozzi-1","path":"/people/chiara-bartoluzzi","__typename":"Platform_Person","_schema":{"label":"Person","pluralLabel":"People"},"organisation":{"id":"65c55d059e947c438698ac98","name":"Italian Institute for Technology"},"position":"Head of Event-Driven Perception for Robotics Group","photo":{"id":"image_gesda-23/person/chiara-bartolozzi-1_photo__Chiara_Bartolozzi_scg95j","url":"https://res.cloudinary.com/shapeable/image/upload/v1694949819/gesda-23/person/chiara-bartolozzi-1_photo__Chiara_Bartolozzi_scg95j.jpg","url2x":null}}],"layout":{"id":"65c55d189e947c438698b059","name":"Invited Contribution","slug":"invited-contribution","component":"PageLayoutInvitedContribution"},"videos":[],"trend":null,"connectedTrends":[],"connectedTopics":[],"connectedSubTopics":[],"topics":[],"events":[],"embeds":{"citations":[],"trends":[],"topics":[],"pages":[],"people":[],"imageAssets":[]},"bannerLinks":[],"cardLinks":[],"children":[],"feedEntries":[],"banner":{"id":"65c55ca69e947c4386989d73","name":"Banner - IC1-3 Robotics","slug":"banner-ic-1-3-robotics","title":null,"description":{"text":""},"alternateText":null,"image":{"id":"image_gesda-23/banner/banner-ic-1-3-robotics_image__Banner_-_IC1-3_Robotics-header_nsogoe","url":"https://res.cloudinary.com/shapeable/image/upload/v1696727249/gesda-23/banner/banner-ic-1-3-robotics_image__Banner_-_IC1-3_Robotics-header_nsogoe.webp","url2x":null,"width":1920,"height":1025,"thumbnails":{"halfBanner":{"url":"https://res.cloudinary.com/shapeable/image/upload/c_limit,w_780/v1696727249/gesda-23/banner/banner-ic-1-3-robotics_image__Banner_-_IC1-3_Robotics-header_nsogoe.webp","url2x":"https://res.cloudinary.com/shapeable/image/upload/c_limit,w_1560/v1696727249/gesda-23/banner/banner-ic-1-3-robotics_image__Banner_-_IC1-3_Robotics-header_nsogoe.webp"}}}},"slices":[]}}},"pageContext":{"lang":{"id":"en","path":"","iso":"en","name":"English","label":"English"},"availableEntities":["Event","Organisation","Page","Person","Post","Space","Topic","Trend","FeedEntry","Citation","OrganisationType","Economy","Video","Objective","ProfileType","ExpertiseLevel","EntityType"],"detailEntities":["Event","Organisation","Page","Person","Post","Space","Topic","Trend"],"site":{"id":"65c55d469e947c438698b5f5","slug":"gesda-radar-website","name":"GESDA Radar Website","url":"https://radar.gesda.global","title":"GESDA","twitter":null,"threads":null,"facebook":null,"linkedin":null,"instagram":null,"flickr":null,"tiktok":null,"youtube":null,"ownerName":"GESDA","recaptchaKey":"6Ld6uogcAAAAAAKB-7dWbOSN1c6YBdzWNiXOwy6Y","googleSiteVerification":"QlQM75enB3lQYYCCQh_ZQuYiT39tm5WmLtz6WSu844U","platformName":"GESDA Community Platform","platformUrl":"https://platform.gesda.global","supportEmail":null,"contactEmail":null,"mainMenu":{"id":"65c55cfe9e947c438698abde","slug":"website-navigation"},"linearMenu":null,"entityViews":[{"id":"6789e470a53c1b39754b99cc_65c55d469e947c438698b5f5","name":"Radar","hash":"radar","label":null,"slug":"radar","count":0,"showCount":null,"disabled":true,"icon":{"id":"6789e46ca53c1b39754b99c8","name":"Radar Icon Glyph","slug":"radar-icon-glyph","component":"RadarIconGlyph"},"childEntityTypes":[],"type":{"id":"6789e309a53c1b39754b99b8","name":"Explorer","slug":"explorer"},"slices":[]},{"id":"6789e688a53c1b39754b9b1c_65c55d469e947c438698b5f5","name":"Pulse of Science","hash":"pulse-of-science","label":null,"slug":"pulse-of-science","count":0,"showCount":null,"disabled":true,"icon":{"id":"6789e685a53c1b39754b9b18","name":"Grid Icon Glyph","slug":"grid-icon-glyph","component":"GridIconGlyph"},"childEntityTypes":[{"id":"65c55ccd9e947c438698a489","name":"Scientific Platform","internalName":"Trend"},{"id":"65c55ccd9e947c438698a488","name":"Topic","internalName":"Topic"},{"id":"65c55ccd9e947c438698a487","name":"SubTopic","internalName":"SubTopic"}],"type":{"id":"6789e309a53c1b39754b99b8","name":"Explorer","slug":"explorer"},"slices":[]},{"id":"6789e6a2a53c1b39754b9b22_65c55d469e947c438698b5f5","name":"Pulse of Diplomacy","hash":"pulse-of-diplomacy","label":null,"slug":"pulse-of-diplomacy","count":0,"showCount":null,"disabled":true,"icon":{"id":"6789e685a53c1b39754b9b18","name":"Grid Icon Glyph","slug":"grid-icon-glyph","component":"GridIconGlyph"},"childEntityTypes":[{"id":"67e36b3d8beecc826617db27","name":"Opportunity","internalName":"Opportunity"}],"type":{"id":"6789e309a53c1b39754b99b8","name":"Explorer","slug":"explorer"},"slices":[]},{"id":"6789e7efa53c1b39754b9c66_65c55d469e947c438698b5f5","name":"Pulse of Impact","hash":"pulse-of-impact","label":null,"slug":"pulse-of-impact","count":0,"showCount":null,"disabled":true,"icon":{"id":"6789e685a53c1b39754b9b18","name":"Grid Icon Glyph","slug":"grid-icon-glyph","component":"GridIconGlyph"},"childEntityTypes":[],"type":{"id":"6789e309a53c1b39754b99b8","name":"Explorer","slug":"explorer"},"slices":[]},{"id":"6789e804a53c1b39754b9c6c_65c55d469e947c438698b5f5","name":"Pulse of Society","hash":"pulse-of-society","label":null,"slug":"pulse-of-society","count":0,"showCount":null,"disabled":true,"icon":{"id":"6789e685a53c1b39754b9b18","name":"Grid Icon Glyph","slug":"grid-icon-glyph","component":"GridIconGlyph"},"childEntityTypes":[],"type":{"id":"6789e309a53c1b39754b99b8","name":"Explorer","slug":"explorer"},"slices":[]},{"id":"6789e819a53c1b39754b9c72_65c55d469e947c438698b5f5","name":"Community","hash":"community","label":null,"slug":"community","count":0,"showCount":null,"disabled":true,"icon":{"id":"6789e685a53c1b39754b9b18","name":"Grid Icon Glyph","slug":"grid-icon-glyph","component":"GridIconGlyph"},"childEntityTypes":[{"id":"65c55ccd9e947c438698a486","name":"Person","internalName":"Person"}],"type":{"id":"6789e309a53c1b39754b99b8","name":"Explorer","slug":"explorer"},"slices":[]},{"id":"6789e82aa53c1b39754b9c78_65c55d469e947c438698b5f5","name":"Resources","hash":"resources","label":null,"slug":"resources","count":0,"showCount":null,"disabled":true,"icon":{"id":"6789e685a53c1b39754b9b18","name":"Grid Icon Glyph","slug":"grid-icon-glyph","component":"GridIconGlyph"},"childEntityTypes":[],"type":{"id":"6789e309a53c1b39754b99b8","name":"Explorer","slug":"explorer"},"slices":[]}],"entityAppViews":[],"entityOnboardingViews":[{"id":"67abe7ff5569917bb935b96e_65c55d469e947c438698b5f5","name":"Trend Expertise","hash":"trend-expertise","label":null,"slug":"trend-expertise","count":0,"showCount":null,"icon":null,"childEntityTypes":[],"type":{"id":"67a43d6819adefbb85f48449","name":"onboarding","slug":"onboarding-100"},"slices":[{"id":"67abe7f45569917bb935b96b_65c55d469e947c438698b5f5","name":"Onboarding Trend Expertise Grid","slug":"onboarding-trend-expertise-grid","label":null,"updated":"2025-04-23T01:40:41.02","layout":{"id":"67abe7aa5569917bb935b969","name":"Onboarding Trend Expertise Grid","slug":"onboarding-trend-expertise-grid","component":"SliceLayoutOnboardingTrendExpertiseGrid"},"connectedEntities":[{"id":"93edb1438351abb2643d4acc","name":"Trend Expertise","slug":"trend-expertise","internalName":"trendExpertise"}],"headerFontType":{"internalName":"serif"},"pretitle":null,"pretitleRich":{"id":"67abe7f45569917bb935b96b_65c55d469e947c438698b5f5_pretitleRich","text":""},"title":null,"titleRich":{"id":"67abe7f45569917bb935b96b_65c55d469e947c438698b5f5_titleRich","text":"Are you **Interested** in any of these Scientific Platforms?"}}]},{"id":"67a44de519adefbb85f48a0e_65c55d469e947c438698b5f5","name":"Profile Types","hash":"profile-types","label":null,"slug":"profile-types-101","count":0,"showCount":null,"icon":null,"childEntityTypes":[],"type":{"id":"67a43d6819adefbb85f48449","name":"onboarding","slug":"onboarding-100"},"slices":[{"id":"67a44ddf19adefbb85f489a7_65c55d469e947c438698b5f5","name":"Onboarding Profile Types Grid","slug":"onboarding-profile-types-grid","label":null,"updated":"2025-04-24T05:29:22.87","layout":{"id":"67a43ed519adefbb85f485f1","name":"Onboarding Profile Types Grid","slug":"onboarding-profile-types-grid-100","component":"SliceLayoutOnboardingProfileTypesGrid"},"connectedEntities":[{"id":"67a8632325822d557814f0ff","name":"Profile Type","slug":"profile-type-100","internalName":"ProfileType"}],"headerFontType":{"internalName":"serif"},"pretitle":"Which of the below best describes you","pretitleRich":{"id":"67a44ddf19adefbb85f489a7_65c55d469e947c438698b5f5_pretitleRich","text":""},"title":"Which best Describes you?","titleRich":{"id":"67a44ddf19adefbb85f489a7_65c55d469e947c438698b5f5_titleRich","text":""}}]},{"id":"67a44dff19adefbb85f48a75_65c55d469e947c438698b5f5","name":"Objectives","hash":"objectives","label":null,"slug":"objectives-101","count":0,"showCount":null,"icon":null,"childEntityTypes":[],"type":{"id":"67a43d6819adefbb85f48449","name":"onboarding","slug":"onboarding-100"},"slices":[{"id":"67a44e1819adefbb85f48a77_65c55d469e947c438698b5f5","name":"Onboarding Objectives Grid","slug":"onboarding-objectives-grid","label":null,"updated":"2025-04-24T00:23:25.08","layout":{"id":"67a43fc719adefbb85f48729","name":"Onboarding Objectives Grid","slug":"onboarding-objectives-grid-100","component":"SliceLayoutOnboardingObjectivesGrid"},"connectedEntities":[{"id":"7b7f9366a36031a0b955f4fe","name":"Objectives","slug":"objectives","internalName":"objectives"}],"headerFontType":null,"pretitle":null,"pretitleRich":{"id":"67a44e1819adefbb85f48a77_65c55d469e947c438698b5f5_pretitleRich","text":""},"title":null,"titleRich":{"id":"67a44e1819adefbb85f48a77_65c55d469e947c438698b5f5_titleRich","text":"What are your priority **objectives**?"}}]}],"gptLanguages":[{"id":"65c55cf89e947c438698ab16","name":"Italian","slug":"italian","iso":"it","locale":null,"path":"","label":null,"menuLabel":null},{"id":"65c55cf89e947c438698ab15","name":"German","slug":"german","iso":"de","locale":null,"path":"","label":null,"menuLabel":null},{"id":"65c55cf89e947c438698ab14","name":"French","slug":"french","iso":"fr","locale":"fr_FR","path":"/fr","label":"Français","menuLabel":null},{"id":"65c55cf89e947c438698ab13","name":"English","slug":"english","iso":"en","locale":"en_US","path":"/en","label":"English","menuLabel":null}],"gptQuestionTemplate":{"id":"66fba3830d8106f0539769a8","name":"Stakeholder Targetted Open Question","slug":"stakeholder-targetted-open-question","path":"/prompt-templates/stakeholder-targetted-open-question","__typename":"PromptTemplate","_entityTypeName":null,"label":"Stakeholder Targetted Question","type":{"id":"66f5044748837661683c4ab2","name":"Ask","slug":"ask"},"description":{"text":"This prompt template is designed to guide the respondent in providing accurate answers based on the provided context while maintaining a specific perspective or tone that aligns with a designated stakeholder type. It emphasizes the importance of acknowledging limitations when sufficient information is not available."},"summary":{"text":"Answer the question based on the provided context, specifying your stakeholder perspective."},"gptModel":null,"variablesPrompt":{"text":""},"submitLabel":null,"languages":[{"id":"65c55cf89e947c438698ab13","name":"English","slug":"english","iso":"en","locale":"en_US"},{"id":"65c55cf89e947c438698ab14","name":"French","slug":"french","iso":"fr","locale":"fr_FR"},{"id":"65c55cf89e947c438698ab15","name":"German","slug":"german","iso":"de","locale":null},{"id":"65c55cf89e947c438698ab16","name":"Italian","slug":"italian","iso":"it","locale":null}],"icon":null,"variables":[{"id":"66fba2d20d8106f0539768b9","name":"question","slug":"question-3","useVectorStore":true,"label":"Question","labelAnother":null,"help":{"text":""},"options":{"text":""},"defaultValue":null,"entityTypes":[],"controlType":{"id":"65c55cbf9e947c438698a336","name":"Textarea","slug":"textarea"},"labelContextual":null,"labelAnotherContextual":null,"helpContextual":{"text":""},"optionsContextual":{"text":""},"defaultValueContextual":null,"entityTypesContextual":[],"controlTypeContextual":null},{"id":"66fba3780d8106f053976906","name":"stakeholder_type","slug":"stakeholder-type","useVectorStore":null,"label":"Stakeholder Type","labelAnother":null,"help":{"text":""},"options":{"text":"A Teenager\nThe General Public\nA Policymaker\nA Scientist"},"defaultValue":"The General Public","entityTypes":[],"controlType":{"id":"65c55cbf9e947c438698a334","name":"Single Select","slug":"select"},"labelContextual":null,"labelAnotherContextual":null,"helpContextual":{"text":""},"optionsContextual":{"text":"A Teenager\nThe General Public\nA Policymaker\nA Scientist"},"defaultValueContextual":null,"entityTypesContextual":[],"controlTypeContextual":null}]},"advertisements":[],"logoVerticalOffset":null,"logoHorizontalOffset":null,"logoVerticalOffsetMobile":null,"logoHorizontalOffsetMobile":null,"logoVerticalOffsetTablet":null,"logoHorizontalOffsetTablet":null,"logoVerticalOffsetDesktop":null,"logoHorizontalOffsetDesktop":null,"logoHeightMobile":37,"logoHeightTablet":40,"logoHeightDesktop":43,"headerHeightMobile":74,"headerHeightTablet":76,"headerHeightDesktop":87,"logo":{"url":"https://res.cloudinary.com/shapeable/image/upload/v1714026063/gesda-platform/site/gesda-website_logo__gesda-color_mx5te2.png","type":"image/png","width":704,"height":316},"logoInverted":{"url":"https://res.cloudinary.com/shapeable/image/upload/v1788137488/gesda-platform/site/gesda-radar-website_logoInverted__gesda-logo_ljyk27.png","type":"image/png","width":403,"height":202},"footerMenu":null,"footerSecondaryMenu":null,"footerContent":{"text":""},"creator":null,"poweredBy":{"id":"65c55d059e947c438698ace1","name":"Shapeable","slug":"shapeable","url":"https://shapeable.ai","logo":{"url":"https://res.cloudinary.com/shapeable/image/upload/v1668986804/gesda-22/organisation/shapeable_logo__shapeable.png","type":"image/png","width":1174,"height":368},"logoInverted":{"url":"https://res.cloudinary.com/shapeable/image/upload/v1731468245/gesda-platform/organisation/shapeable_logoInverted__shapeable-logo-inverted_rpx1ou.png","type":"image/png","width":392,"height":118},"logoSubtle":null},"poweredByLabel":null,"poweredByContent":{"text":""},"explorerPage":{"name":"Home","title":null,"slug":"home","path":"/"},"termsPage":null,"homePage":{"name":"Home","title":null,"slug":"home","path":"/"},"knowledgeHubPage":null,"privacyPolicyPage":{"name":"Privacy Policy","title":null,"slug":"privacy-policy","path":"/privacy-policy"},"summary":{"text":"Humankind is facing global challenges that are placing people and the planet under stress and in great uncertainty.\n\nAt the same time, breakthroughs in science and technology are occurring at an unprecedented pace – but the full ramifications of these breakthroughs are not always evident.\n\nAnticipation, therefore, is key to building the future by fully leveraging the potential of new science and technology advances to improve well-being and promote inclusive development.\n\nThe Geneva Science and Diplomacy Anticipator (GESDA) Foundation was created in 2019 in Geneva to tackle this issue, as a think tank and do tank."},"thumbnail":{"url":"https://res.cloudinary.com/shapeable/image/upload/v1760413449/gesda-platform/site/gesda-radar-website_thumbnail__Screenshot_2025-10-14_at_11.42.59_o2cgxm.png"},"openGraph":{"title":"GESDA","date":"2026-09-16T00:16:49.08","description":{"plain":"Humankind is facing global challenges that are placing people and the planet under stress and in great uncertainty.\nAt the same time, breakthroughs in science and technology are occurring at an unprecedented pace – but the full ramifications of these breakthroughs are not always evident.\nAnticipation, therefore, is key to building the future by fully leveraging the potential of new science and technology advances to improve well-being and promote inclusive development.\nThe Geneva Science and Diplomacy Anticipator (GESDA) Foundation was created in 2019 in Geneva to tackle this issue, as a think tank and do tank.\n"},"image":{"url":"https://res.cloudinary.com/shapeable/image/upload/v1760413449/gesda-platform/site/gesda-radar-website_thumbnail__Screenshot_2025-10-14_at_11.42.59_o2cgxm.png","type":"image/png","thumbnails":{"full":{"url":"https://res.cloudinary.com/shapeable/image/upload/v1760413449/gesda-platform/site/gesda-radar-website_thumbnail__Screenshot_2025-10-14_at_11.42.59_o2cgxm.png"}}}},"termsAndConditions":{"text":""},"privacyPolicy":{"text":"At the Geneva Science and Diplomacy Anticipator (hereafter referred to as ‘GESDA’), we respect your privacy. We want to ensure that you get the information, content, and experiences that matter most to you. GESDA is committed to protecting the privacy of its stakeholders, communities, and other contacts.\n\n## Scope\n\nThis privacy policy applies to all personal data processed by full-time and part-time employees, volunteers when acting on behalf of GESDA, contractors and partners doing business on behalf of GESDA, as well as all legal entities, all operating locations in all countries, and all business processes conducted by GESDA.\n\n## Information Collected\n\n#### What information do we collect?\n\nGESDA collects the following personal data in line with the use purposes explained in a subsequent section:\n\n  * Your name and contact details\n  * Online profile data/usage\n  * Contact information\n  * Social media profile information\n  * Education and professional information\n  * Registration and participation in GESDA events and activities - Information about service usage\n  * Cookies\n  * Authentication data\n  * Location information\n  * Author and peer review information\n  * Other information you upload or provide to us\n\n#### How do we use your information?\n\nGESDA uses (and, where specified, shares) your personal information for the following purposes:\n\n  * To provide support or other services. GESDA may use your personal information to provide you with support or other services that you have ordered or requested. GESDA may also use your personal information to respond directly to your requests for information, including registrations for webinars, or other specific requests, or pass your contact information to the appropriate GESDA supplier or reseller for further follow-up related to your interests.\n  * To provide information based on your needs and respond to your requests. GESDA may use your personal information to provide you with notices of new product releases and service developments.\n  * To administer products. GESDA may contact you if you make use of (digital) products we offer, to confirm certain information (for example, that you did not experience problems in a download process). We may also use this information to confirm compliance with licensing and other terms of use and may share it with your company/institution.\n  * To assist in your participation in GESDA activities. GESDA will communicate with you, if you are participating in certain GESDA activities such as the GESDA Summit, authoring or reviewing a GESDA article, or GESDA humanitarian activities. GESDA may send you information such as update messages related to those activities (such as but not limited to the event's content, and event logistics)\n  * To update you on relevant GESDA events and opportunities. GESDA may communicate with you regarding relevant GESDA events and opportunities.\n  * To protect GESDA content and services. We may use your information to prevent potentially illegal activities and to enforce our terms and conditions.\n  * To get feedback or input from you. In order to deliver products and services of most interest to our stakeholders, from time to time, we may ask you to provide us input and feedback (for example through surveys).\n\n#### How can you control your information?\n\nYou can control the information we have about you and how we use as follows:\n\n  * If you are a registered guest for the GESDA Annual Summit 2021, any request for review, revise or correction of your personal data can be sent to summit@global.gesda specifying your request.\n\n#### Personal data about minors and children\n\nGESDA does not knowingly collect data from or about children under 16 without the permission of parent(s)/guardian(s). If we learn that we have collected personal information from a child under 16, we will delete that information as quickly as possible. If you believe that we might have any information from or about a child under age 16, please contact us.\n\n#### How will you know if the Privacy Policy is changed?\n\nGESDA may update its Privacy Policy from time to time. If we make any material changes you will be notified by means of a notice on our website prior on the date the change becomes effective. We encourage you to periodically review this page for the latest information on our privacy practices.\n\n## Technical And Regulatory Information\n\n#### Logging practices\n\nGESDA automatically records the Internet Protocol (IP) addresses of visitors. The IP address is a unique number assigned to every computer on the internet. Generally, an IP address changes each time you connect to the internet (it is a \"dynamic\" address). Note, however, that if you have a broadband connection, depending on your individual circumstance, the IP address that we collect may contain information that could be deemed identifiable. This is because, with some broadband connections, your IP address doesn't change (it is \"static\") and could be associated with your personal computer.\n\nAs well as recording the IP addresses of users, GESDA may also keep track of sites that users visited immediately prior to visiting GESDA's website and the search terms they used to find it. We keep track of the pages visited on GESDA's website, the amount of time spent on those pages and the types of searches done on them. Your searches remain confidential and anonymous. GESDA uses this information only for statistical purposes to find out which pages users find most useful and to improve the website.\nGESDA also captures and stores information that you transmit. This may include:\n\n  * Browser/Device type/version\n  * Operating system used\n  * Media Access Control (MAC) address\n  * Date and time of the server request\n  * Volume of data transferred\n\n#### External links behaviour\n\nSome of the links on GESDA's websites link to other sites created and maintained by other public- and/or private-sector organizations. GESDA provides these links solely for your information and convenience. When you transfer to an outside website, you are leaving the GESDA domain, and GESDA's information management policies no longer apply. GESDA encourages you to read the privacy statement of each external website that you visit before you provide any personal data.\n\n#### Cookies and web beacons\n\nCookies and web beacons are electronic placeholders that are placed on your device by websites to track your individual movements on that website over time. GESDA uses both session-based cookies (which last only for the duration of the user's session) and persistent cookies (which remain on your device and provide information about the session you are in and waits for the next time you use that site again).\n\nThese cookies and web beacons provide useful information to GESDA, enabling us to recognize repeat users, facilitate the user's access to and use of our sites, allows us to track usage behavior, and to balance the usage of our websites on all GESDA web servers.\nTracking cookies, third-party cookies, and other technologies such as web beacons may be used to process additional information, enable non-core functionalities on the GESDA website and enable third-party functions (such as a social media \"share\" link). We may also include web beacons and other similar technology in promotional email messages to determine whether the messages have been opened.\n\n#### Do Not Track (DNT)\n\nThe online advertising industry has self-regulatory initiatives designed to provide consumers a choice in the types of ads they may see online and to conveniently opt-out from online behavioral ads served by some or all of the companies participating in these programs. Our websites do not respond to DNT consumer browser settings.\n\n#### Responses to legal requests\n\nGESDA reserves the right to share your information to respond to duly authorized information requests of governmental authorities or where required by law.\n\n#### Your data rights\n\nGESDA complies with all applicable data privacy laws and regulations including, but not limited to, the General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA). Under these laws and regulations, you may have certain rights to your data. Should you wish to exercise any of these rights, please send an email request to info@gesda.global with \"Data Privacy Request'' in the subject line and in the email please identify the specific privacy right you request assistance with. Please note additional information may be requested prior to fulfilling a request and that GESDA reserves the right to charge a fee, where permitted, to cover the cost of certain requests.\n\n#### How do I contact you if there is an issue?\nIf you have any questions or concerns about this Privacy Policy or about the use of your personal information, please feel free to contact us by email at info@gesda.global\n\n#### Contact Information\nGeneva Science and Diplomacy Anticipator (GESDA) c/o Fondation Campus Biotech\nChemin des Mines 9\n1202 Geneva\n+41 58 201 02 61\ninfo@gesda.global\n"},"welcomeUrl":"https://radar.gesda.global/app/welcome","welcomeTitle":"Welcome to GESDA and thanks for joining us! ","invitationAction":"join the GESDA Radar Website community","setupCompletionMessage":null,"languages":[],"brandColors":[],"showLogin":true,"showShareMenu":null,"showFollowMenu":null,"showPlatformLogin":null,"showContactUs":null,"loginLabel":"Member login","platformLoginLabel":null,"headerButtons":[{"id":"6a8fc65ae69c51827b086a0b","name":"Gesda Public Website link","slug":"gesda-public-website-link","page":null,"label":"Public website","url":"https://www.gesda.global","isDownload":null,"color":null,"icon":null},{"id":"68eda7e8501d12182c0ef374","name":"Ai Modal Button","slug":"ai-modal-button","page":null,"label":"RadarAI","url":null,"isDownload":null,"color":null,"icon":{"id":"6789e346a53c1b39754b99bd","name":"AI Lower Case Icon Glyph","slug":"ai-lower-case-icon-glyph","component":"AiLowerCaseIconGlyph"}}]},"includeProfile":true,"disableProfileEditing":false,"dynamicEntityTypeNames":[],"profilePath":"/app/profile","welcomePath":"/app/welcome","id":"65c55d169e947c438698b026","slug":"robotics-and-the-challenge-of-embodied-intelligence","entityPath":"/publications/robotics-and-the-challenge-of-embodied-intelligence","name":"Robotics and the Challenge of Embodied Intelligence","openGraph":{"title":"Robotics and the Challenge of Embodied Intelligence","date":"2026-09-14T03:11:43.78","description":{"plain":"The idea that we will soon have robots capable of performing many of the same tasks as humans has become commonplace, thanks to their depiction in films, television and books. However, the design of robots that can sense and process information about their environment, then use this information to make decisions about their own behaviour, remains a major and multi-faceted research and engineering challenge.\n"},"image":{"url":"https://res.cloudinary.com/shapeable/image/upload/v1696727257/gesda-23/banner/banner-ic-1-3-robotics_thumbnail__Banner_-_IC1-3_Robotics-thumb_lhjh0u.webp","type":"image/webp","thumbnails":{"full":{"url":"https://res.cloudinary.com/shapeable/image/upload/v1696727257/gesda-23/banner/banner-ic-1-3-robotics_thumbnail__Banner_-_IC1-3_Robotics-thumb_lhjh0u.webp"}}}},"entityTypeName":"Page","updated":"2026-09-14T03:11:43.78","__type":"Page"}},
    "staticQueryHashes": ["1044227382","1071300789","1158597448","1242646999","1427075558","1452322194","1520036161","1586309863","1606754935","1734835243","1816168740","1903214493","1989845544","2034981229","2097402925","2181044613","239250450","2409034939","2463401854","2612382444","2862279633","2910164142","2912920178","2955142335","3005265507","3073584486","3124191253","3172506128","3320076387","3624873332","364221563","3665990662","3692255024","3778988535","3782604890","4091857177","4216505212","701411134","758936535","881103158"]}