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SBIR Fast-Track: Scalable Robotic Skill Learning via Generative Embodied Twins and Vision-Language-Action Models for Task-Oriented Autonomy

Awarded

2537982Grant

Contract Overview

Solicitation details, issuing organization, response deadlines, documents, and interested companies for this government contract opportunity.

AI Contract Overview

This Small Business Innovation Research Fast-Track project, funded by the National Science Foundation, focuses on developing scalable and intelligent robotic assistance for high-frequency physical work in commercial buildings. The primary goal is to create a world-model-enabled data engine and learning system that allows robots to execute complex, long-horizon service workflows across multiple similar sites with minimal site-specific programming and human intervention. By integrating a customizable generative world model with a hierarchical vision-language-action architecture and operational memory, the project aims to enhance the reliability and safety of autonomous systems in real-world environments. The technical approach involves collecting facility scans and interaction logs to build executable world models for training and stress testing, alongside training hierarchical policies that combine learned skill primitives with real-world demonstrations. This innovation seeks to advance the scientific understanding of how to translate AI and robotics progress into practical autonomy, specifically regarding the creation of digital representations from operational data and the maintenance of system safety as they evolve. The project holds significant commercial potential due to the ubiquitous and repeatable nature of commercial building workflows.

General Info

NSF-funded project developing scalable, intelligent robotic assistance for commercial building service workflows.

Agency

National Science Foundation → Start-ups and Small Businesses Section

Contract Value

$400,000

NAICS

541715 - Research and Development in the Physical, Engineering, and Life Sciences (except Nanotechnology and Biotechnology)

Place of Performance

TN, USA

Set-Aside

NONE

Awardee

AI SCOPE INC

Award Issued Date

09/09/2026, 12:00:00 AM UTC

Documents

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Timeline

PhaseAwarded

PostedSep 9

Award Notice

AwardedSep 9

Contract was awarded

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Organization & Contact Information

AgencyNational Science Foundation → Start-ups and Small Businesses Section

Contacts1 person available

OfficeN/A

Organization / AgencyNational Science Foundation → Start-ups and Small Businesses Section

Office AddressN/A

Contacts

Peter AthertonProgram Officer

contact@example.gov

+1 (555) 000-0000

Full Description

The broader impact of this Small Business Innovation Research (SBIR) Fast-Track project is safe, scalable, and intelligent robotic assistance for high-frequency physical work in commercial buildings. The commercial potential is substantial because these workflows are ubiquitous and repeatable, enabling scalable multi-site adoption. The innovation also advances scientific and technological understanding by developing new methods to translate recent AI and robotics advances into real-world autonomy: how to construct executable digital representations of real scenarios from operational data, how to train and stress-test robot skills across different sites; and how to maintain reliability and safety as systems evolve. This Small Business Innovation Research (SBIR) Fast-Track project will develop a world-model-enabled data engine and learning system that enables robots to execute long-horizon, high-frequency service workflows reliably across many similar real-world sites. The research objective is to develop long-horizon robot skill while reducing site-specific programming and operational intervention by integrating a site-customizable generative world model with a hierarchical vision-language-action (VLA) architecture augmented by operational memory. The proposed research will (1) collect facility scans and robot interaction logs to build and continuously refine an executable world model that generates physically valid, task-relevant scenario distributions for training and stress testing; and (2) train hierarchical VLA policies that combine learned skill primitives with demonstrations and real rollouts, while incorporating verification, recovery, and human-in-the-loop supervision to maintain safety and consistency over time. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.