Company
Portfolio Data
Back to Company SearchBOARDWALK ROBOTICS, INC.
UEI: LN78V4K2WL53
Number of Employees: 11
HUBZone Owned: No
Woman Owned: No
Socially and Economically Disadvantaged: No
SBIR/STTR Involvement
Year of first award: 2021
2
Phase I Awards
1
Phase II Awards
50%
Conversion Rate
$267,858
Phase I Dollars
$1,800,000
Phase II Dollars
$2,067,858
Total Awarded
Awards
HEFESTUS: Humanoid Enhancement Framework for Efficient and Safe Task-oriented Unmanned Sustainment
Amount: $1,800,000 Topic: AF242-D012
HEFESTUS (Humanoid Enhancement Framework for Efficient and Safe Task-oriented Unmanned Sustainment) is an advanced robotics project aimed at transforming sustainment tasks within the United States Air Force. By developing the humanoid robot Hephaestus, we seek to enhance efficiency, safety, and readiness in sustainment operations. This project will leverage cutting-edge technologies in control algorithms, learning from demonstration (LfD), and behavior tree-based mission autonomy to create a robust, versatile robotic platform capable of performing complex tasks autonomously.Hephaestus will be designed to maximize its manipulation workspace through kinematic optimization techniques and quick tool-changing mechanisms, enabling it to handle a wide array of tasks. The learning framework will utilize teleoperated demonstrations to train generative models for motion primitives and skill coordination, ensuring the robot can perform intricate tasks with high precision and adaptability. The integration of advanced whole-body Model Predictive Control (MPC) algorithms will enable precise and dynamic task execution, enhancing the robot's athletic intelligence.The project will be organized into four main thrusts: (1) Hardware for Maximum Workspace, (2) Learning from Demonstration for Task Intelligence, (3) Whole-Body Athletic Intelligence, and (4) Behavior Trees for Mission Intelligence. These efforts will culminate in a fully functional Hephaestus prototype capable of performing tasks such as using tools like sanders and drills, pushing carts loaded with parts, and loading and unloading parts for maintenance operations.The final demonstration will be conducted at Warner Robins Air Logistics Complex (WR-ALC), showcasing Hephaestus's ability to navigate the facility and perform various sustainment tasks. This project aims to achieve a Technology Readiness Level (TRL) of 7 by the end of Phase II, setting the stage for further advancements and commercialization in Phase III.By integrating mission, task, and athletic intelligence, Hephaestus will significantly reduce the burden on human personnel, enhance safety, and improve the efficiency of depot-level maintenance operations. The innovative learning techniques and robust control systems developed in this project will also have wide-ranging applications in commercial sectors, driving innovation and economic growth.
Tagged as:
SBIR
Phase II
2025
DOW
USAF
Handler: An End-to-End Tool for Semantic-based Manipulation using Affordance Templates
Amount: $156,499 Topic: Z5
We propose to research, develop, and demonstrate Handler, an autonomous semantic detection, planning, and grasping affordance module capable of fast online inference and adaptation, as well as continuous learning and improvement. Handler will work by combining semantic and primitive pose-recognition algorithms with a rich affordance template library and online grasp-finding algorithms. Uniquely, it will also feature a pipeline for expanding its semantic recognition and affordance library through continuous learning using state-of-the-art mesh generation tools. It will then estimate grasping locations from learned grasp generation models using these created meshes.nbsp;Handler will consist of four primary tools:Handler Environment Constructor, whichnbsp;will leverage state-of-the-art object classifier and pose extraction algorithms to automatically create a digital twin of the real environment and the objects within.nbsp;Handler Dynamic Affordance Template Library, which is a database of modifiable objects that encode anbsp;mesh and defined object interactions, including candidate grasp locations and suggested trajectories between grasp points. These templates can be applied to objects found by the environment constructor, andnbsp;govern how they can be manipulated by robots.Handler User Interface, whichnbsp;allows users to view the digital twin of the environment and manage the affordance library, includingnbsp;tweaking existing affordance templates, adjusting upcoming interactions or grasp methods, or capturing perception and semantic data for construction of new templates.nbsp;Handler Affordance Builder, which can automatically create object meshes from captured video and use them to both train pose estimation and semantic classifier networks, as well as create new affordance templates using automatic grasp calculators.
Tagged as:
SBIR
Phase I
2023
NASA
SupplyBot: Extreme Mobility Resupply Robot
Amount: $111,359 Topic: A20-132
We propose to research, develop, and demonstrate SupplyBot: A humanoid robot with mobility capabilities that allow it to resupply Soldiers in extreme environments, which cannot be reached by traditional ground vehicles. SupplyBot will deliver supplies by autonomously traversing narrow trails, scrambling up rocky hills, opening doors, squeezing through narrow spaces, crawling through windows and mouse holes, climbing stairs and ladders, stepping over obstacles, and crawling underneath obstacles. It will help extend the “tip of the spear” by reducing the security requirements for resupply and enhancing the situation awareness of the last 100 meters, while providing 24 hour resupply capabilities. The robot will have commercial applications in the dangerous industry of confined space inspection, maintenance, painting, and repair.
Tagged as:
SBIR
Phase I
2021
DOW
ARMY