Thrust 2: Machine Learning and Artificial Intelligence
As mentioned in Thrust 1, my interest lies in the Human Journey. And so my research into machine learning and AI explore not how to replace humans but how to enhance them. The project outlined below is the one that was accepted for TRISH's GoForLaunch program and so it is one of my primary areas of focus for the coming year (12/2020-12/2021).
Machine Learning as a Countermeasure to Spatial Disorientation in a Spaceflight Analog Environment
Spaceflights, such as to the Moon and Mars, will cause many sensorimotor related difficulties that could jeopardize the mission. For example, if astronauts are forced to land manually onto the surface of Mars, they will experience a rapid gravitational transition while dynamically stabilizing the spacecraft. In low-g and 0g environments, gravitationally dependent vestibular and somatosensory cues are minimized and astronauts can easily become spatially disoriented. In our prior work we have used machine learning to predict performance and have developed an effective training program that enhances every person’s performance. However, very little has been done where machine learning algorithms identify the approach into instability and where it can provide feedback for optimal joystick control in a spaceflight analog condition that reliably produces spatial disorientation. Future work will focus on developing a real-time human-machine interaction to mitigate effects of spatial disorientation. This work is in collaboration with Dr. Pengyu Hong, a Computer Science professor at Brandeis who specializes in machine learning. This proposal is relevant to the following NASA roadmap gaps: HHC1, HHC2, SM103, SM202, HSIA401, HSIA701
Specific Aim 1: Using machine learning to predict destabilizing joystick deflections, loss of control and crashes in a spaceflight analog environment.
Blindfolded subjects will balance themselves in a device programmed with inverted pendulum dynamics. They will be placed in an orientation where they cannot use gravitational cues and where they become spatially disoriented. We will use a variety of different machine learning techniques, such as recurrent artificial neural networks, to determine how early we can predict the occurrence of what would be a potentially fatal mission ending event, such as destabilizing joystick deflections, loss of control, and crashes. These findings could be translated into more realistic flight training simulations for development of a warning system that could alert an astronaut in real time before a critical mistake is about to be made.
Specific Aim 2: Using machine learning to develop an optimal model controller that can suggest the best joystick deflection.
We will use machine learning to predict the next optimal joystick deflection. Because our dataset is relatively sparse for the purpose of creating a full representation of the solution space, we will innovate new ways of using prior knowledge and using Bayesian techniques to build the machine learning model. These innovations will allow machine learning techniques to be relevant for NASA projects that do not have big data. In the future, we will be able to provide real time feedback where a computer and human are in the loop, and where the computer helps in learning and performance in a disorienting spaceflight analog condition.
I would love to find collaborators in any field including: computer science, AI coder, Game Developer, Math/Physics
Generalization of our machine learning method to human postural balancing. As with any experiment, there is always the question whether the findings are specific to the experimental paradigm or whether it can generalize to other systems. In the Ashton Graybiel Spatial Orientation Lab there are other investigators who research human postural balancing and my future goal is to use a similar machine learning implementation to predict loss of control in human postural balancing in adverse and novel conditions.
Training an AI agent to balance: I would love to create a balancing game (on a cellphone, tablet or computer) where the human has to first train an AI agent in a simple balancing task. Then, the Human-AI team have to balance in more complex situations where there are multiple suboptimal solutions and only one optimal solution. I would love to map the solution space and see how different people explore the space. I want to use the individual differences in solution space exploration as a metric and see if it correlates with how people explore the solution space in my spaceflight condition. As mentioned in the previous section, I would also love to see whether individual differences in anxiety and stress predict how a person explores this solution space.