Beyond Low Earth Orbit: Biological Research, Artificial Intelligence,
and Self-Driving Labs
- URL: http://arxiv.org/abs/2112.12582v1
- Date: Wed, 22 Dec 2021 05:18:26 GMT
- Title: Beyond Low Earth Orbit: Biological Research, Artificial Intelligence,
and Self-Driving Labs
- Abstract summary: Space biology research aims to understand fundamental effects of spaceflight on organisms.
Bioengineer spacecraft and habitats to stabilize the ecosystem of plants, crops, microbes, animals, and humans for sustained multi-planetary life.
- Score: 0.8855198354664937
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Space biology research aims to understand fundamental effects of spaceflight
on organisms, develop foundational knowledge to support deep space exploration,
and ultimately bioengineer spacecraft and habitats to stabilize the ecosystem
of plants, crops, microbes, animals, and humans for sustained multi-planetary
life. To advance these aims, the field leverages experiments, platforms, data,
and model organisms from both spaceborne and ground-analog studies. As research
is extended beyond low Earth orbit, experiments and platforms must be maximally
autonomous, light, agile, and intelligent to expedite knowledge discovery. Here
we present a summary of recommendations from a workshop organized by the
National Aeronautics and Space Administration on artificial intelligence,
machine learning, and modeling applications which offer key solutions toward
these space biology challenges. In the next decade, the synthesis of artificial
intelligence into the field of space biology will deepen the biological
understanding of spaceflight effects, facilitate predictive modeling and
analytics, support maximally autonomous and reproducible experiments, and
efficiently manage spaceborne data and metadata, all with the goal to enable
life to thrive in deep space.
Related papers
- Legged Autonomous Surface Science In Analogue Environments (LASSIE): Making Every Robotic Step Count in Planetary Exploration [5.928856661424766]
We present two novel approaches to explore planetary surfaces.<n>One is high-mobility legged robots that use direct surface interactions to collect rich information about the terrain's mechanics.<n>The other is human-inspired data acquisition algorithms that enable robots to reason about scientific hypotheses and adapt exploration priorities.
arXiv Detail & Related papers (2026-03-20T05:55:53Z) - AI4X Roadmap: Artificial Intelligence for the advancement of scientific pursuit and its future directions [65.44445343399126]
We look at AI-enabled science across biology, chemistry, climate science, mathematics, materials science, physics, self-driving laboratories and unconventional computing.<n>Several shared themes emerge: the need for diverse and trustworthy data, transferable electronic-structure and interatomic models, AI systems integrated into end-to-end scientific synthesis.<n>Across domains, we highlight how large foundation models, active learning and self-driving laboratories can close loops between prediction and validation.
arXiv Detail & Related papers (2025-11-26T02:10:28Z) - Autonomous Agents for Scientific Discovery: Orchestrating Scientists, Language, Code, and Physics [82.55776608452017]
Large language models (LLMs) provide a flexible and versatile framework that orchestrates interactions with human scientists, natural language, computer language and code, and physics.<n>This paper presents our view and vision of LLM-based scientific agents and their growing role in transforming the scientific discovery lifecycle.<n>We identify open research challenges and outline promising directions for building more robust, generalizable, and adaptive scientific agents.
arXiv Detail & Related papers (2025-10-10T22:26:26Z) - Understanding Exoplanet Habitability: A Bayesian ML Framework for Predicting Atmospheric Absorption Spectra [0.0]
We are working to create an atmospheric absorption spectrum prediction model for exoplanets.<n>The model will be based on both collected observational spectra and synthetic spectral data.<n>This work is expected to contribute to a better understanding of exoplanetary properties and general exoplanet climates and habitability.
arXiv Detail & Related papers (2025-10-09T19:34:07Z) - A Self-Evolving AI Agent System for Climate Science [59.08800209508371]
We introduce EarthLink, the first self-evolving AI agent system designed as an interactive "copilot" for Earth scientists.<n>Through natural language interaction, EarthLink automates the entire research workflow by integrating planning, code execution, data analysis, and physical reasoning.<n>It exhibits human-like cross-disciplinary analytical ability and proficiency comparable to a junior researcher in expert evaluations on core large-scale climate tasks.
arXiv Detail & Related papers (2025-07-23T08:29:25Z) - Position: Intelligent Science Laboratory Requires the Integration of Cognitive and Embodied AI [98.19195693735487]
We propose the paradigm of Intelligent Science Laboratories (ISLs)<n>ISLs are a multi-layered, closed-loop framework that deeply integrates cognitive and embodied intelligence.<n>We argue that such systems are essential for overcoming the current limitations of scientific discovery.
arXiv Detail & Related papers (2025-06-24T13:31:44Z) - From Automation to Autonomy: A Survey on Large Language Models in Scientific Discovery [67.07598263346591]
Large Language Models (LLMs) are catalyzing a paradigm shift in scientific discovery.<n>This survey systematically charts this burgeoning field, placing a central focus on the changing roles and escalating capabilities of LLMs in science.
arXiv Detail & Related papers (2025-05-19T15:41:32Z) - Science Autonomy using Machine Learning for Astrobiology [3.305775349086079]
In recent decades, artificial intelligence (AI) and machine learning (ML) have become vital for space missions enabling rapid data processing, advanced pattern recognition, and enhanced insight extraction.
These tools are especially valuable in astrobiology applications, where models must distinguish biotic patterns from complex abiotic backgrounds.
arXiv Detail & Related papers (2025-04-01T12:20:18Z) - Scaling Laws in Scientific Discovery with AI and Robot Scientists [72.3420699173245]
An autonomous generalist scientist (AGS) concept combines agentic AI and embodied robotics to automate the entire research lifecycle.
AGS aims to significantly reduce the time and resources needed for scientific discovery.
As these autonomous systems become increasingly integrated into the research process, we hypothesize that scientific discovery might adhere to new scaling laws.
arXiv Detail & Related papers (2025-03-28T14:00:27Z) - How to Build the Virtual Cell with Artificial Intelligence: Priorities and Opportunities [46.671834972945874]
We propose a vision of leveraging advances in AI to construct virtual cells.
We discuss desired capabilities of such AI Virtual Cells, including generating universal representations of biological entities.
We envision a future where AI Virtual Cells help identify new drug targets, predict cellular responses to perturbations, as well as scale hypothesis exploration.
arXiv Detail & Related papers (2024-09-18T02:41:50Z) - LLM and Simulation as Bilevel Optimizers: A New Paradigm to Advance Physical Scientific Discovery [141.39722070734737]
We propose to enhance the knowledge-driven, abstract reasoning abilities of Large Language Models with the computational strength of simulations.
We introduce Scientific Generative Agent (SGA), a bilevel optimization framework.
We conduct experiments to demonstrate our framework's efficacy in law discovery and molecular design.
arXiv Detail & Related papers (2024-05-16T03:04:10Z) - ProBio: A Protocol-guided Multimodal Dataset for Molecular Biology Lab [67.24684071577211]
The challenge of replicating research results has posed a significant impediment to the field of molecular biology.
We first curate a comprehensive multimodal dataset, named ProBio, as an initial step towards this objective.
Next, we devise two challenging benchmarks, transparent solution tracking and multimodal action recognition, to emphasize the unique characteristics and difficulties associated with activity understanding in BioLab settings.
arXiv Detail & Related papers (2023-11-01T14:44:01Z) - The Future of Fundamental Science Led by Generative Closed-Loop
Artificial Intelligence [67.70415658080121]
Recent advances in machine learning and AI are disrupting technological innovation, product development, and society as a whole.
AI has contributed less to fundamental science in part because large data sets of high-quality data for scientific practice and model discovery are more difficult to access.
Here we explore and investigate aspects of an AI-driven, automated, closed-loop approach to scientific discovery.
arXiv Detail & Related papers (2023-07-09T21:16:56Z) - Automated Scientific Discovery: From Equation Discovery to Autonomous
Discovery Systems [5.7923858184309385]
The paper surveys automated scientific discovery, from equation discovery and symbolic regression to autonomous discovery systems and agents.
We will present closed-loop scientific discovery systems, starting with the pioneering work on the Adam system up to current efforts in fields from material science to astronomy.
The maximal level, level five, is defined to require no human intervention at all in the production of scientific knowledge.
arXiv Detail & Related papers (2023-05-03T16:35:41Z) - Onboard Science Instrument Autonomy for the Detection of Microscopy
Biosignatures on the Ocean Worlds Life Surveyor [2.526702791640305]
The quest to find extraterrestrial life is a critical scientific endeavor with civilization-level implications.
The lack of a precise definition of life poses a fundamental challenge to formulating detection strategies.
We describe two OSIA implementations developed as part of the Ocean Worlds Life Surveyor prototype instrument suite at the Jet Propulsion Laboratory.
arXiv Detail & Related papers (2023-04-25T23:10:02Z) - Artificial Intelligence and Natural Language Processing and
Understanding in Space: Four ESA Case Studies [48.53582660901672]
We present a methodological framework based on artificial intelligence and natural language processing and understanding to automatically extract information from Space documents.
Case studies are implemented across different functional areas of ESA, including Mission Design, Quality Assurance, Long-Term Data Preservation, and the Open Space Innovation Platform.
arXiv Detail & Related papers (2022-10-07T15:50:17Z) - A Low-Cost Robot Science Kit for Education with Symbolic Regression for
Hypothesis Discovery and Validation [15.72286703649173]
Next generation of physical science involves robot scientists - autonomous physical science systems capable of experimental design, execution, and analysis in a closed loop.
To build and use these systems, the next generation workforce requires expertise in diverse areas including ML, control systems, measurement science, materials synthesis, decision theory, among others.
We present the next generation in science education, a kit for building a low-cost autonomous scientist.
arXiv Detail & Related papers (2022-04-08T17:25:28Z) - Beyond Low Earth Orbit: Biomonitoring, Artificial Intelligence, and
Precision Space Health [0.8838373492847601]
We propose an appropriately autonomous and intelligent Precision Space Health system.
It will monitor, aggregate, and assess biomedical statuses.
It will analyze and predict personalized adverse health outcomes.
arXiv Detail & Related papers (2021-12-22T05:33:15Z) - Seeing biodiversity: perspectives in machine learning for wildlife
conservation [49.15793025634011]
We argue that machine learning can meet this analytic challenge to enhance our understanding, monitoring capacity, and conservation of wildlife species.
In essence, by combining new machine learning approaches with ecological domain knowledge, animal ecologists can capitalize on the abundance of data generated by modern sensor technologies.
arXiv Detail & Related papers (2021-10-25T13:40:36Z) - On-the-fly Closed-loop Autonomous Materials Discovery via Bayesian
Active Learning [12.021024778717575]
We focus a closed-loop, active learning-driven autonomous system on the discovery of advanced materials.
We demonstrate autonomous research methodology that can place complex, advanced materials in reach.
This robot science enables science-over-the-network, reducing the economic impact of scientists being physically separated from their labs.
arXiv Detail & Related papers (2020-06-11T01:26:24Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.