\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":"The majority of design to manufacturing today is still an ad hoc and empirical process. 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They have been shown to create complex and interesting forms with interactive feedback from a human user, and also have been shown to create high performing solutions when left to optimize product design towards one or more objective feature. Examples of EA performance include a part deployed on the NASA ST5 spacecraft which outperformed all of the alternative human designed solutions or specialized crumple zones in industry leading automobiles. The universal design tool I propose will take advantage of computational precision and speed to create an array of high performing designs, then take advantage of a user's human intuition to judge and select designs according to intuitive properties that the computer cannot compute on its own (e.g. designs that are novel, interesting, attractive, or promising). The fact that different pressures are shaping the design from different systems (the user's preferences and the computer's performance metric) will overcome many of the problems with local optima and lack of diversity that EAs have faced in the past. Additionally, recent work has shown that the use of drivers such as interestingness or novelty can be more effective drivers of EAs than a direct performance metric. The iterative design process of an EA means that there is always a functionally viable, fully described, design that is being optimized. Thus at any point in time, the best current design can be sent to a 3D printer for immediate fabrication. This design tool would contain properties such as: Immediate and individualized fabrication potential of current designs; Lack of need for domain specific knowledge or for full intuition of the (usually non-linear) constraints and processes occurring (and being optimized) at multiple scales; And the sole requirements of a high level performance metric and general human intuition. These make this system ideal for design optimization at any scale - whether it be as large and rigorous as a NASA spacecraft or as quick and simple as a cleaning robot at home or a load bearing frame at an industrial job site. 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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":"The majority of design to manufacturing today is still an ad hoc and empirical process. 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They have been shown to create complex and interesting forms with interactive feedback from a human user, and also have been shown to create high performing solutions when left to optimize product design towards one or more objective feature. Examples of EA performance include a part deployed on the NASA ST5 spacecraft which outperformed all of the alternative human designed solutions or specialized crumple zones in industry leading automobiles. The universal design tool I propose will take advantage of computational precision and speed to create an array of high performing designs, then take advantage of a user's human intuition to judge and select designs according to intuitive properties that the computer cannot compute on its own (e.g. designs that are novel, interesting, attractive, or promising). The fact that different pressures are shaping the design from different systems (the user's preferences and the computer's performance metric) will overcome many of the problems with local optima and lack of diversity that EAs have faced in the past. Additionally, recent work has shown that the use of drivers such as interestingness or novelty can be more effective drivers of EAs than a direct performance metric. The iterative design process of an EA means that there is always a functionally viable, fully described, design that is being optimized. Thus at any point in time, the best current design can be sent to a 3D printer for immediate fabrication. 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Additionally, since the design process is iterative and based off of rigorous simulation and validation from a performance model, all products that come out of this system will have a digital twin - a simulated version upon which additional tests, circumstances, or project mirroring can take place.","benefits":"This design tool would contain properties such as: Immediate and individualized fabrication potential of current designs; Lack of need for domain specific knowledge or for full intuition of the (usually non-linear) constraints and processes occurring (and being optimized) at multiple scales; And the sole requirements of a high level performance metric and general human intuition. These make this system ideal for design optimization at any scale - whether it be as large and rigorous as a NASA spacecraft or as quick and simple as a cleaning robot at home or a load bearing frame at an industrial job site. Additionally, since the design process is iterative and based off of rigorous simulation and validation from a performance model, all products that come out of this system will have a digital twin - a simulated version upon which additional tests, circumstances, or project mirroring can take place.","releaseStatus":"Released","status":"Completed","destinationType":["Foundational_Knowledge"],"trlBegin":2,"trlCurrent":3,"trlEnd":3,"favorited":false,"detailedFunding":false,"programContacts":[],"endDateString":"Jul 2017","startDateString":"Aug 2013"},"technologyOutcomeDate":"2017-07-31","technologyOutcomePath":"Closed_Out","details":"The work done under this grant was intended to explore the relationship between the morphology (body) and controller (brain) of an autonomous embodied robot – especially as it related to the optimal design of both of these subcomponents of the agent. This work was explored in the context of soft robotics, not only for its wide array of important applications (e.g. human robot interaction, navigation of unpredictable terrain, efficient grasping and object manipulation), but also for the complexity of its embodiment (soft materials have a theoretically infinite degrees of freedom). This complex embodiment was hypothesized to enable soft robots to learn/perform tasks more efficiently by offloading computation from their brains to be performed by their bodies (called morphological computation [1-3]). While the concept of morphological computation is critical to efficient robot design, it has been rarely demonstrated, and poorly measured previous to this grant (often relying on anecdotal and qualitative descriptions). Over the course of the grant, I demonstrated the optimization of efficient robots that employed morphological computation in a variety of different tasks and environments (e.g. running [4, 5], swimming [6], squeezing [7], and reaching [8]), as well as the rigorous and quantitative measurement of this phenomenon [9]. As this is a general phenomenon for embodied machines like robots, the desctiption and measurement of this property is expected to inform the design of robotics applications more generally than the soft robot implementation employed in this work (e.g. NASA tensegrity robots). The relationship between the body and brain of an autonomous robot also presents itself in the interdependence of the two during the robot's behavior and learning. The interdependence of brain and body is an accepted idea in cognitive psychology under the term “embodied cognition” [10, 11], but is rarely appreciated in the field of robotics. During this grant, I also quantitatively demonstrated that the current state-of-the-art methods in the design of robotic morphologies had ignored the implications of embodied cognition on optimization, resulting in premature convergence of optimization trajectories on suboptimal solutions [12]. I then designed an optimization algorithm for autonomous embodied machines that accounted for this phenomenon, and demonstrated that is was able to avoid the premature convergence that had affected methods up to this point, and resulted in better performing robots [13]. While this finding has only been demonstrated in my soft robot platform up to this point, early and anecdotal findings have shown it to scale well to other instances of robotic optimization – and I am optimistic that this algorithm may serve as a general purpose design software system for autonomous robotic design. The same general applicability can be said for the importance of incorporating concepts from cognitive psychology to robotic design – as is strongly implied by the work under this grant.","infoText":"Closed out","infoTextExtra":"Project closed out","isIndirect":false,"infusionPretty":"","isBiDirectional":false,"technologyOutcomeDateFullString":"July 2017","technologyOutcomeDateString":"Jul 2017","technologyOutcomePartnerPretty":"","technologyOutcomePathPretty":"Closed Out","technologyOutcomeRationalePretty":""}],"libraryItems":[{"files":[],"libraryItemId":363786,"title":"Project Website","libraryItemType":"Link","url":"https://www.nasa.gov/directorates/spacetech/home/index.html","projectId":91444,"internalOnly":false,"publishedDateString":"","entryDateString":"01/22/25 01:10 AM","libraryItemTypePretty":"Link","modifiedDateString":"10/25/24 02:23 PM"},{"files":[],"libraryItemId":363787,"title":"Topological evolution for embodied cellular automata","libraryItemType":"Link","url":"http://dx.doi.org/10.1016/j.tcs.2015.06.024","projectId":91444,"isPrimary":false,"internalOnly":false,"publishedDateString":"","entryDateString":"01/28/25 02:02 AM","libraryItemTypePretty":"Link","modifiedDateString":"01/28/25 02:02 AM"}],"states":[{"abbreviation":"NY","country":{"abbreviation":"US","countryId":236,"name":"United States"},"countryId":236,"name":"New York","stateTerritoryId":55,"isTerritory":false}],"endDateString":"Jul 2017","startDateString":"Aug 2013"}}