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Such a system would be pretrained on the knowledgebase of clinical knowledge on Earth, minimizing the amount of Earth data that needs to be transferred into space. Then during deployment, the system would be constantly refined through active learning from diverse streams of data from sensors in the spacecraft, data collected daily from individual astronauts, and human-in-the-loop feedback from the crew. The model could be interrogated for predictions and recommendations on personalized crew health based on the overall status of the spacecraft, medicinal stores, and status of other crew members. Adaptation techniques would be used to incorporate spaceflight data that have very different distributions from the training data due to the extreme environment. Edge computing and the most advanced neuromorphic processing would enable computation in scenarios with low power and bandwidth, while dimensionality reduction would be employed to ensure that the input data streams from spaceflight are as small as possible.\nIn order to realize this long-term vision, several hardware and software aspects need to be developed and assembled. First, models pretrained on Earth biomedical data would need to be evaluated for predictive accuracy, and the best one selected. That model would need to be adapted to learn from diverse, sparse, and inconsistently measured data streams, as well as human-in-the-loop feedback. A data integration, standardization, and dimensionality reduction methodology would need to be developed to handle all data types and feed them into the model. Once the software and data infrastructure is developed, it would need to be integrated with small footprint compute processors and tested in high-radiation, high-vibration, unregulated temperature situations. \n\tAs a short-term goal, we recommend to focus on the development of the data and model software structure. Several large language models (LLM) already exist that have been trained on Earth biomedical and clinical knowledgebases, including BioMedLLM, Med-PaLM, SPOKE LLM, and Foresight. These models need to be evaluated for accuracy and the best one chosen for a proof-of-concept structure, while maintaining awareness of the accelerating AI field and incorporating any newly improved model architectures as needed. Then, we recommend to develop a database of synthetic data types to mimic the diverse data streams that are expected in a long-duration space mission. This should include environmental and microbial data from the spacecraft, non-invasive data from wearables and point-of-care devices employed by astronauts, and more invasive molecular and physiological monitoring of clinical and biomarker data from astronauts. The data standardization methodology should be developed, and these data streams used to refine the clinical LLM. Several scenarios should be developed that could plausibly come up in a long-duration space mission, and changes or aberrations introduced to the data at specific times to mimic these scenarios. 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It pairs artificial intelligence with human intelligence in order to create a seamless solution that allows autonomous drive to be viable and marketable. They have created a Mobility Manager function which is a laborer who executed human-in-the-loop functions within SAM system. Nissan invited the HAT Lab to discuss NASA's lessons learned on remote operator support and to participate in a workshop discussion of one of their use cases. 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Also addressed are a knowledge base for real-time diagnosis, a pilot-in-the-loop simulation of an orbital docking maneuver, intelligent perturbation algorithms for space scheduling optimization, a fuzzy control method for a space manipulator system, hyperredundant manipulator applications, robotic servicing of EOS instruments, and a summary of astronaut inputs on automation and robotics for the Space Station Freedom.","title":"i-SAIRAS '90; Proceedings of the International Symposium on Artificial Intelligence, Robotics and Automation in Space, Kobe, Japan, Nov. 18-20, 1990","stiType":"CONFERENCE_PROCEEDINGS","distribution":"PUBLIC","submittedDate":"2013-08-15T14:17:00.0000000+00:00","isLessonsLearned":false,"disseminated":"METADATA_ONLY","stiTypeDetails":"Conference 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Frequently, these tasks involve articulating objects such as opening or closing a valve. However, existing human-in-the-loop methods for registering objects do not consider articulations and the corresponding impact to the geometry of the object, which can cause the methods to fail. In this work, we present an approach where the registration system attempts to automatically determine the object model, pose, and articulation for user-selected points using a nonlinear iterative closest point algorithm. When the automated fitting is incorrect, the operator can iteratively intervene with corrections after which the system will refit the object. We present an implementation of our fitting procedure and evaluate it with a user study that\nshows that it can improve user performance, in measures of time on task and task load, ease of use, and usefulness compared to a manual registration approach. We also present a situated\nexample that demonstrates the integration of our method in an end-to-end system for articulating a remote valve.","isLessonsLearned":false,"disseminated":"DOCUMENT_AND_METADATA","cui":{"isCui":false,"submissionId":20220013506,"id":"664bae36971b491d86a8a4abb6ae113e"},"meetings":[{"country":"JP","submissionId":20220013506,"endDate":"2022-10-27T05:00:00.0000000+00:00","sponsors":[{"organizationId":"1e665bedc00a5085af284bcc4cd5c4fd","meta":{"organization":{"name":"Institute of Electrical and Electronics Engineers","location":"Paris, France"}},"meetingId":"9bc1b5ccdb99486bb84d3ff3a65b4806","id":"749abc2240ba45d2b6e11f393fedc275"}],"name":" International Conference on Intelligent Robots and Systems (IROS 2022)","location":"Kyoto","id":"9bc1b5ccdb99486bb84d3ff3a65b4806","url":"https://iros2022.org/","startDate":"2022-10-23T05:00:00.0000000+00:00"}],"publications":[{"submissionId":20220013506,"id":"aeff4ef215044f539159be6886d0214e","publicationDate":"2022-10-23T05:00:00.0000000+00:00"}],"status":"CURATED","related":[],"downloads":[{"draft":false,"mimetype":"video/mp4","name":"IROS_video.mp4","type":"STI","links":{"original":"/api/citations/20220013506/downloads/IROS_video.mp4","fulltext":"/api/citations/20220013506/downloads/IROS_video.mp4.txt"}}],"downloadsAvailable":true,"index":"submissions-2026-11-05-06-21"},{"_meta":{"score":63.01158},"copyright":{"thirdPartyPermissionsProduced":false,"disclosedToPublic":false,"containsIndication":false,"publisherPermissionOrRightsToDistribute":false,"belongsToUsGov":false,"determinationType":"GOV_PERMITTED","thirdPartyContentCondition":"NOT_SET","belongsToContractor":false,"disclosedInvention":false,"licenseType":"NO","submissionId":20230013649,"containsThirdPartyMaterial":false,"belongsToPublisher":false,"id":"3ceb136352d94b4584e4cd1e3e18c8b5","belongsToAuthors":false},"subjectCategories":["Cybernetics, Artificial Intelligence and Robotics"],"keywords":["Robotic Assembly","In-space servicing"],"exportControl":{"isExportControl":"NO","submissionId":20230013649,"ear":"NO","id":"522edbb9e27a48bc9d64705eec158d90","itar":"NO"},"distributionDate":"2023-10-01T04:00:00.0000000+00:00","fundingNumbers":[{"number":"000278","submissionId":20230013649,"id":"6d43eb25a26e4a7a8301672b743679fe","type":"WBS"}],"title":"Hardware-in-the-Loop Simulation of Modular Antenna Assembly","stiType":"POSTER","distribution":"PUBLIC","submittedDate":"2023-09-20T16:54:38.1294980+00:00","authorAffiliations":[{"sequence":0,"submissionId":20230013649,"meta":{"author":{"orcidId":"","name":"A. 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