Orbital Outpost X is developing technologies for autonomy, machine learning, and human-machine teaming in deep space. Their presentation discusses: 1. Using virtual reality to prototype astronaut training with redirected walking and mission control dashboards. 2. Developing tele-robotics for space assembly and analog visual smart sensors inspired by honeybee neurology and cognition for autonomous systems. 3. The key challenges of implementing true autonomy at the extreme edge of space through intelligence at the sensor level and developing analog, brain-inspired neuromorphic computing solutions.

1. 23-25 OCTOBER 2023 | LAS VEGAS, NV
Copyright © [Miguel Arias/ Orbital Outpost X, Inc]. Published by the American Institute of Aeronautics and Astronautics, Inc. with permission.
How will autonomy, machine learning, & human
machine teaming be implemented in deep space?
Orbital Outpost X, Inc.

2. How will autonomy, machine learning, & human
machine teaming be implemented in deep space?
Miguel Arias, PhD, XR Technology Lead
Orbital Outpost X, Inc.
October 25th, 2023

3. Content
1. Overview
2. VR prototyping
1. Mission demo and RW
2. Dashboard
3. Tele-robotics
4. RoWo PoC
5. Key factors for more autonomy
6. Analog Sensors for Autonomy

4. 1. Overview: Astronaut training in XR with Redirected Walking
• NASA STTR Phase I
• OOX and NYU

5. 23-25 OCTOBER 2023 | LAS VEGAS, NV

6. 2.1 Redirected Walking

8. 2.2. Mission Control Dashboard

10. 3. Telerobotics

11. 4. Telerobotics for Space Assembly

12. 5. Key to More Autonomous Space Systems
• Intelligence at the Extreme Edge
• Autonomy vs Semi-Supervised Operation
(True Autonomy within Parameters vs Status Check, Command, Repeat)
• Smart Sensors – Highest computational processing at the Edge

13. Analog Visual
Smart Sensors
• We leverage ~ 60 million years of evolution
• And a wealth of current basic science research into honeybee neurology
• Honeybee rice grain sized brains use micro-watts for 6 degrees of flight freedom, exact
target acquisition & predator avoidance in turbulent & noisy environments – all in real time
responses to environment.
• And better than GPS location, PNT with mm accuracy, 4 phase memory (like humans), &
very strong auditory, visual, chemical, & tactile sensing & communications capabilities re
target location, value, flight time, & path
• SWAP (size, weight, power) first target is 10x reduction each parameter
• Active research and design began CY 2021, TRL 3

14. Autonomy problem at the extreme
edge
• Current autonomous/extreme edge sensor platforms have limited capabilities,
without adding mass, volume, power & often backhaul/ processing latency.
• Neuromorphic Computing (NMC - brain like or neuron like computing) today is
based on processing architectures that do not exist in nature
• Digital, not analog.
• Analog is nature’s way.
• NMC not optimal for very low power, light-weight, small, autonomous, remote
applications at extreme edge in noisy, i.e. real environments for aerospace,
defense, and commercial applications

15. 6. Conclusions
• Multidisciplinary Approach:
• VR/Nvidia Omniverse modeling and simulation
• Virtual Reality with Physics simulation for prototyping and
validation
• AI/ML and Robotics algorithms
• Data generation to train algorithms
• Sensors Simulation -> Smart sensors

16. • Miguel Arias, PhD, XR Technology Lead
• ariasm@orbitaloutpostx.com
• Orbital Outpost X, Inc.
Thanks!