\tThe Space Technology Research Grants Program will accelerate the development of "push" technologies to support the future space science and exploration needs of NASA, other government agencies and the commercial space sector. Innovative efforts with high risk and high payoff will be encouraged. The program is composed of two competitively awarded components.
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Data triage (finding the few frames containing a curious object on a planet's surface out of terabytes of data) becomes more important. In the last few years, research into a class of scalable unsupervised algorithms, also called deep learning algorithms, has blossomed, in part due to state of the art performance in a number of areas. A common thread among many recent deep learning algorithms is that they tend to represent the world in ways similar to how our brains represent the world. For example, thanks to decades of work by neuroscientists, we now know that in the V1 area of the visual cortex, the first region that visual information passes through after the retina, neurons tune themselves to respond to oriented edges and do so in a way that groups them together based on similarity. With this behavior as a goal, researchers set out to devise simple algorithms that reproduce this effect. It turns out that there are several. 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","parentProgram":{"ableToSelect":false,"isActive":true,"description":"Catalyst is a portfolio of early stage programs that specialize in different innovation constituencies and mechanisms to push the state of the art in aerospace technology development","programId":92327,"responsibleMd":{"canUserEdit":false,"locationEdit":false,"organizationRolePretty":"","organizationTypePretty":""},"title":"Catalyst","manageGaps":false,"acronymOrTitle":"Catalyst"},"parentProgramId":92327,"programId":69,"responsibleMd":{"organizationId":4875,"organizationName":"Space Technology Mission Directorate","acronym":"STMD","organizationType":"NASA_Mission_Directorate","canUserEdit":false,"locationEdit":false,"organizationRolePretty":"","organizationTypePretty":"NASA Mission Directorate"},"responsibleMdOffice":4875,"stockImageFileId":36658,"title":"Space Technology Research Grants","manageGaps":false,"acronymOrTitle":"STRG"},"description":"Though we dream of the day when humans will first walk on Mars, these dreams remain in the distance. 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Data triage (finding the few frames containing a curious object on a planet's surface out of terabytes of data) becomes more important. In the last few years, research into a class of scalable unsupervised algorithms, also called deep learning algorithms, has blossomed, in part due to state of the art performance in a number of areas. A common thread among many recent deep learning algorithms is that they tend to represent the world in ways similar to how our brains represent the world. For example, thanks to decades of work by neuroscientists, we now know that in the V1 area of the visual cortex, the first region that visual information passes through after the retina, neurons tune themselves to respond to oriented edges and do so in a way that groups them together based on similarity. With this behavior as a goal, researchers set out to devise simple algorithms that reproduce this effect. It turns out that there are several. One, known as Topographic Independent Component Analysis, has each neuron start with random connections and then look for patterns that are statistically out of the ordinary. When it finds one, it locks onto this pattern, discouraging other neurons from duplicating its findings but simultaneously trying to group itself with other neurons that have learned patterns which are similar, but not identical. My proposed research plan is to develop existing and new unsupervised learning algorithms of this type and apply them to a robotic system. Specifically, I will demonstrate a prototype system capable of (1) learning about itself and its environment and of (2) actively carrying out experiments to learn more about itself and its environment. Research will be kept focused by developing a system aimed at eventual deployment on an unmanned space mission. Key components of the project will include synthetic data experiments, experiments on data recorded from a real robot, and finally experiments with learning in the loop as the robot explores its environment and learns actively. The unsupervised algorithms in question are applicable not only to a single domain, but to creating models for a wide range of applications. Thus, advances are likely to have far-reaching implications for many areas of autonomous space exploration. Tantalizing though this is, it is equally exciting that unsupervised learning is already finding application with surprisingly impressive performance right now, indicating great promise for near-term application to unmanned space exploration.","benefits":"The unsupervised algorithms in question are applicable not only to a single domain, but to creating models for a wide range of applications. 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“A robust and competitive commercial space sector is vital to continued progress in space. The United States is committed to encouraging and facilitating the growth of a U.S. commercial space sector that supports U.S. needs, is globally competitive, and advances U.S. leadership in the generation of new markets and innovation-driven entrepreneurship.”
Flight Opportunities directly answers the call of the President’s policy through the acquisition of suborbital launch services on commercial suborbital launch vehicles. By purchasing flight opportunities on U.S. commercial vehicles the Flight Opportunities program is encouraging and facilitating the growth of this market while simultaneously providing pathways to advance the technology readiness of a wide range of new launch vehicle and space technologies.
One of the greatest challenges NASA faces in incorporating advanced technologies into future missions is bridging the mid-technology readiness level (TRL) (4-7) gap (or “valley of death”), between component or prototype testing in a lab or ground facility setting, and the final infusion of a new technology into critical path exploration or science mission development. To cross this gap, the proposed new technology must pass system level testing in a relevant operational environment. Maturing a space technology to flight readiness status through relevant environment testing is a significant challenge from cost, schedule, and technical risk perspectives.
FO has its lineage from the former Innovative Partnership Program (IPP) of FY09, specifically the Facilitated Access to the Space Environment for Technology (FAST) project and the Commercial Reusable Suborbital Research (CRuSR) project. The FAST and CRuSR activities are continued within the FO Program, as the parabolic and suborbital, flight campaigns, respectively. The flights will provide opportunities to expose new technologies to low-g environments and/or high altitude environments. The intent is to demonstrate and mature various technologies for future applications. These emerging technologies will come from the nine other programs within the Space Technology Mission Directorate, from the other Mission Directorates and external sources (other Government Agencies, Academia, and Commercial Industries.
The NASA Flight Opportunities (FO) Program has been established as a part of the Space Technologies Mission Directorate (STMD) to rapidly develop, demonstrate and infuse revolutionary, high-payoff technologies through transparent, collaborative partnerships, expanding the boundaries of the aerospace enterprise by providing the nation’s investments in space technologies to make a difference in the world around us. FO focuses on maturation of technologies that are of benefit to multiple customers, to flight readiness status with an outcome of Technology Readiness Level (TRL) 6 or higher. These crosscutting capabilities are those that advance multiple future aerospace missions, including flight projects where near-space or in-space demonstration is needed before the capability can transition to direct mission application. Maturing technologies to a higher TRL status through relevant flight opportunities testing is a significant challenge from both a cost and risk perspective.
","programId":72,"responsibleMd":{"organizationId":4875,"organizationName":"Space Technology Mission Directorate","acronym":"STMD","organizationType":"NASA_Mission_Directorate","canUserEdit":false,"locationEdit":false,"organizationRolePretty":"","organizationTypePretty":"NASA Mission Directorate"},"responsibleMdOffice":4875,"stockImageFileId":36656,"title":"Flight Opportunities","manageGaps":false,"acronymOrTitle":"FO"},"description":"Fission power provides game-changing solutions for powering advanced NASA missions. Radiators are needed to reject waste heat from Fission Power Systems (FPS). Titanium – water heat pipes are being considered for use in the radiators of a fission power system option for lunar exploration. Embedded in the radiators and deployed on the surface, heat pipes would be oriented vertically and would operate as thermosyphons, a subset of heat pipes that have no wick in their condenser. Their design is determined in part by the flooding limit which is attributed to the interfacial shear force at the boundary between liquid and vapor, and occurs when concurrent vapor flow is so severe that liquid flow is prevented. Flooding is determined by the thickness of the fluid film on the walls and the interaction of fluid flow with concurrent vapor counter flow, both inversely proportional to gravity. The planned test objective of this project is to validate the gravity-dependent flooding limit model for thermosyphons.\tThe Space Technology Research Grants Program will accelerate the development of "push" technologies to support the future space science and exploration needs of NASA, other government agencies and the commercial space sector. Innovative efforts with high risk and high payoff will be encouraged. The program is composed of two competitively awarded components.
","parentProgram":{"ableToSelect":false,"isActive":true,"description":"Catalyst is a portfolio of early stage programs that specialize in different innovation constituencies and mechanisms to push the state of the art in aerospace technology development","programId":92327,"responsibleMd":{"canUserEdit":false,"locationEdit":false,"organizationRolePretty":"","organizationTypePretty":""},"title":"Catalyst","manageGaps":false,"acronymOrTitle":"Catalyst"},"parentProgramId":92327,"programId":69,"responsibleMd":{"organizationId":4875,"organizationName":"Space Technology Mission Directorate","acronym":"STMD","organizationType":"NASA_Mission_Directorate","canUserEdit":false,"locationEdit":false,"organizationRolePretty":"","organizationTypePretty":"NASA Mission Directorate"},"responsibleMdOffice":4875,"stockImageFileId":36658,"title":"Space Technology Research Grants","manageGaps":false,"acronymOrTitle":"STRG"},"description":"Though we dream of the day when humans will first walk on Mars, these dreams remain in the distance. 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Data triage (finding the few frames containing a curious object on a planet's surface out of terabytes of data) becomes more important. In the last few years, research into a class of scalable unsupervised algorithms, also called deep learning algorithms, has blossomed, in part due to state of the art performance in a number of areas. A common thread among many recent deep learning algorithms is that they tend to represent the world in ways similar to how our brains represent the world. For example, thanks to decades of work by neuroscientists, we now know that in the V1 area of the visual cortex, the first region that visual information passes through after the retina, neurons tune themselves to respond to oriented edges and do so in a way that groups them together based on similarity. With this behavior as a goal, researchers set out to devise simple algorithms that reproduce this effect. It turns out that there are several. One, known as Topographic Independent Component Analysis, has each neuron start with random connections and then look for patterns that are statistically out of the ordinary. When it finds one, it locks onto this pattern, discouraging other neurons from duplicating its findings but simultaneously trying to group itself with other neurons that have learned patterns which are similar, but not identical. My proposed research plan is to develop existing and new unsupervised learning algorithms of this type and apply them to a robotic system. Specifically, I will demonstrate a prototype system capable of (1) learning about itself and its environment and of (2) actively carrying out experiments to learn more about itself and its environment. Research will be kept focused by developing a system aimed at eventual deployment on an unmanned space mission. Key components of the project will include synthetic data experiments, experiments on data recorded from a real robot, and finally experiments with learning in the loop as the robot explores its environment and learns actively. The unsupervised algorithms in question are applicable not only to a single domain, but to creating models for a wide range of applications. Thus, advances are likely to have far-reaching implications for many areas of autonomous space exploration. Tantalizing though this is, it is equally exciting that unsupervised learning is already finding application with surprisingly impressive performance right now, indicating great promise for near-term application to unmanned space exploration.","benefits":"The unsupervised algorithms in question are applicable not only to a single domain, but to creating models for a wide range of applications. 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I identified early on the importance of using deep learning methods for attacking this problem and then spent the next few years developing new algorithms for generative modeling (unsupervised learning) as well as for supervised learning. Specific contributions to the field are given below. In total, the contributions bring us significantly closer to being able to deploy capable deep learning models on off-world robots Notable accomplishments during the fellowship fall under the following eight projects, all of which involve improvements to neural network modeling or understanding. Generative Stochastic Networks: these are a general family of generative models for which we proved important properties, enabling much further work and greater understanding of generative modeling. Our 2013 paper [1] has been cited 121 times. Transfer Learning: we carefully measured transfer learning performance and uncovered several properties not previously appreciated, like fragile co-adaptation between neurons on neighboring layers. Our 2014 paper [2] has been cited 252 times. Fooling Deep Neural Networks: we showed provocative findings that neural nets can be easily fooled using a variety of methods, results which generated much discussion in the field. Our 2014/2015 paper [3] has been cited 163 times and was listed as one of the top 100 articles of the year across all fields of science (alongside, e.g., the paper reporting water on Mars). Optimization-based visualization (visualization of neurons): we've obtained several important results in optimization-based visualization, including setting the state of the art several times (2015 ICML DL workshop [4], 2016 ICML Workshop on Visualization for Deep Learning [5], and 2016 arXiv and to-be NIPS paper [6]). Facial-keypoint recognition models: we set the start of the art for three keypoint datasets using a new type of network dubbed the Recombinator Network [7]. Understanding Convergent Learning: our paper measuring inter-network differences and similarities, showing how internal representations could be local or distributed, was given an oral at ICLR [8] and opened the path for a new style of research in interpreting network behavior by comparing training across networks. Deep Visualization Toolbox (visualization of activations): we released a set of open-source tools for visualizing neural networks on both static images (e.g. jpgs loaded from disk) as well as live video frames (e.g. a feed from the user's webcam). Our 2015 paper [4] has been cited 48 times, and the GitHub repository for the project has been starred 836 times. Automated Agricultural Disease Phenotyping: in collaboration with the researchers from the Cornell Plant Sciences department, my adviser (Hod Lipson) and I wrote and obtained an NSF grant for $1.1m to use deep learning techniques to automatically detect and map agricultural disease spread via Unmanned Aircraft System (UAS) robots.","infoText":"Closed out","infoTextExtra":"Project closed out","isIndirect":false,"infusionPretty":"","isBiDirectional":false,"technologyOutcomeDateFullString":"July 2016","technologyOutcomeDateString":"Jul 2016","technologyOutcomePartnerPretty":"","technologyOutcomePathPretty":"Closed Out","technologyOutcomeRationalePretty":""}],"libraryItems":[{"files":[],"libraryItemId":364529,"title":"Project Website","libraryItemType":"Link","url":"https://www.nasa.gov/directorates/spacetech/home/index.html","projectId":91575,"internalOnly":false,"publishedDateString":"","entryDateString":"01/22/25 01:10 AM","libraryItemTypePretty":"Link","modifiedDateString":"10/25/24 02:23 PM"}],"states":[{"abbreviation":"NY","country":{"abbreviation":"US","countryId":236,"name":"United States"},"countryId":236,"name":"New York","stateTerritoryId":55,"isTerritory":false}],"endDateString":"Jul 2016","startDateString":"Aug 2013"}}