Vine robots are a class of soft continuum robots that grow via tip eversion, allowing them to move their tip without relying on reaction forces from the environment. Constructed from compliant materials such as fabric and thin, flexible plastic, these robots are able to grow many times their original length with the use of fluidic pressure. They can be mechanically programmed/preformed to follow a desired path during growth by changing the structure of their body prior to deployment. We present a model for fabricating preformed vine robots with discrete bends. We apply this model across combinations of three fabrication methods and two materials. One fabrication method, taping folds into the robot body, is from the literature. The other two methods, welding folds and connecting fasteners embedded in the robot body, are novel. Measurements show the ability of the resulting vine robots to follow a desired path and show that fabrication method has a significant impact. Results include bend angles with as little as 0.12 degrees of error, and segment lengths with as low as 0.36 mm of error. The required growth pressure and average growth speed of these preformed vine robots ranged from 11.5 to 23.7kPA and 3.75 to 10 cm/s, respectively. These results validate the use of preformed vine robots for deployment along known paths, and serve as a guide for choosing a fabrication method and material combination based on the specific needs of the task.

Driven by the need for parallelizable hyperparameter optimization methods, this paper studies \emph{open loop} search methods: sequences that are predetermined and can be generated before a single configuration is evaluated. Examples include grid search, uniform random search, low discrepancy sequences, and other sampling distributions. In particular, we propose the use of $k$-determinantal point processes in hyperparameter optimization via random search. Compared to conventional uniform random search where hyperparameter settings are sampled independently, a $k$-DPP promotes diversity. We describe an approach that transforms hyperparameter search spaces for efficient use with a $k$-DPP. In addition, we introduce a novel Metropolis-Hastings algorithm which can sample from $k$-DPPs defined over any space from which uniform samples can be drawn, including spaces with a mixture of discrete and continuous dimensions or tree structure. Our experiments show significant benefits in realistic scenarios with a limited budget for training supervised learners, whether in serial or parallel.

In decision-theoretic planning problems, such as (partially observable) Markov decision problems or coordination graphs, agents typically aim to optimize a scalar value function. However, in many real-world problems agents are faced with multiple possibly conflicting objectives. In such multi-objective problems, the value is a vector rather than a scalar, and we need methods that compute a coverage set, i.e., a set of solutions optimal for all possible trade-offs between the objectives. In this project propose new multi-objective planning methods that compute the so-called convex coverage set (CCS): the coverage set for when policies can be stochastic, or the preferences are linear. We show that the CCS has favorable mathematical properties, and is typically much easier to compute that the Pareto front, which is often axiomatically assumed as the solution set for multi-objective decision problems.