A Low-Cost Manipulator for Space Research and Undergraduate Engineering Education

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/83596/1/AIAA-2010-3394-549.pd

Towards Guaranteeing Safe and Efficient Human-Robot Collaboration Using Human Intent Prediction

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/97120/1/AIAA2012-5317.pd

Physically-Proximal Human-Robot Collaboration: Enhancing Safety and Efficiency Through Intent Prediction

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/76049/1/AIAA-2009-1950-840.pd

Investigating the chromatin dynamics of gene activation

Enhancers are cis-regulatory elements which contribute to the activation of gene expression. The spatio-temporal control of gene expression is particularly important during embryonic development, when the expression of developmental regulators is tightly controlled. Sonic hedgehog (Shh) is an important signalling protein which is vital for the patterning of the embryo. Shh is expressed throughout the developing central nervous system, gut and the posterior limb bud. This complex pattern of expression is regulated by the activity of multiple tissue-specific enhancers which are spread through a 1 Mb genomic desert. Many of these enhancers activate Shh expression over large genomic distances, with some enhancers being located within the intron of neighbouring genes. The textbook model for enhancer-mediated gene activation suggests that an enhancer moves within close proximity to its target promoter, recruiting transcription factors and RNA polymerase II to the gene and promoting transcription. However, recent studies have brought this model into question. Understanding the dynamics of enhancers and promoters during transcriptional activation is vital for comprehending how gene expression is regulated by enhancers. Advances in techniques enabling the labelling and tracking of non-repetitive loci in live cells have allowed this to start to be addressed. To study how Shh expression is regulated by its enhancers in live cells, firstly I needed to develop a system where Shh expression could be activated in cultured cells. It was known that Shh expression could be activated in mouse embryonic stem cells (mESCs) through retinoic acid treatment; however, the enhancer responsible for activating Shh expression and the cell type the mESCs differentiate into were previously unknown. I investigated changes in chromatin accessibility and modifications to show that Shh expression is activated from endodermal enhancers when mESCs are differentiated with retinoic acid. RNA-seq confirmed that the resulting cells express several markers of early endoderm and mesoderm lineages. The identification of enhancers which activate Shh expression in this mESC differentiation system allowed the tagging and tracking of these loci using a CRISPR-based live cell DNA imaging system. I developed a system that is versatile, simple and stable, with a view to decrease the number of guide RNAs required in order to visualise non-repetitive loci. The dynamics of the Shh promoter and were determined in cells where Shh was transcriptionally silent or active. I found that the dynamics of these cis-regulatory elements were sub-diffusive despite gene activity. Overall, through quantitative CRISPR-imaging, I found direct measurements for chromatin mobility of cis-regulatory elements in living cells under different states of activity

Human Intent Prediction Using Markov Decision Processes

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/140661/1/1.i010090.pd

Safe and Efficient Robot Action Choice Using Human Intent Prediction in Physically-Shared Space Environments.

Emerging robotic systems are capable of autonomously planning and executing well-defined tasks, particularly when the environment can be accurately modeled. Robots supporting human space exploration must be able to safely interact with human astronaut companions during intravehicular and extravehicular activities. Given a shared workspace, efficiency can be gained by leveraging robotic awareness of its human companion. This dissertation presents a modular architecture that allows a human and robotic manipulator to efficiently complete independent sets of tasks in a shared physical workspace without the robot requiring oversight or situational awareness from its human companion. We propose that a robot requires four capabilities to act safely and optimally with awareness of its companion: sense the environment and the human within it; translate sensor data into a form useful for decision-making; use this data to predict the human’s future intent; and then use this information to inform its action-choice based also on the robot’s goals and safety constraints. We first present a series of human subject experiments demonstrating that human intent can help a robot predict and avoid conflict, and that sharing the workspace need not degrade human performance so long as the manipulator does not distract or introduce conflict. We describe an architecture that relies on Markov Decision Processes (MDPs) to support robot decision-making. A key contribution of our architecture is its decomposition of the decision problem into two parts: human intent prediction (HIP) and robot action choice (RAC). This decomposition is made possible by an assumption that the robot’s actions will not influence human intent. Presuming an observer that can feedback human actions in real-time, we leverage the well-known space environment and task scripts astronauts rehearse in advance to devise models for human intent prediction and robot action choice. We describe a series of case studies for HIP and RAC using a minimal set of state attributes, including an abbreviated action-history. MDP policies are evaluated in terms of model fitness and safety/efficiency performance tradeoffs. Simulation results indicate that incorporation of both observed and predicted human actions improves robot action choice. Future work could extend to more general human-robot interaction.PhDAerospace EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/107160/1/cmcghan_1.pd