Learning from failures in robot-assisted feeding: Using online learning to develop manipulation strategies for bite acquisition

Successful robot-assisted feeding requires bite acquisition of a wide variety of food items. Different food items may require different manipulation actions for successful bite acquisition. Therefore, a key challenge is to handle previously-unseen food items with very different action distributions. By leveraging contexts from previous bite acquisition attempts, a robot should be able to learn online how to acquire those previously-unseen food items. In this ongoing work, we construct a contextual bandit framework for this problem setting. We then propose variants of the $\epsilon$-greedy and LinUCB contextual bandit algorithms to minimize cumulative regret within that setting. In future, we expect empirical estimates of cumulative regret for each algorithm on robot bite acquisition trials as well as updated theoretical regret bounds that leverage the more structured context of this problem setting.

Informed Sampling for Asymptotically Optimal Path Planning (Consolidated Version)

Anytime almost-surely asymptotically optimal planners, such as RRT*, incrementally find paths to every state in the search domain. This is inefficient once an initial solution is found as then only states that can provide a better solution need to be considered. Exact knowledge of these states requires solving the problem but can be approximated with heuristics. This paper formally defines these sets of states and demonstrates how they can be used to analyze arbitrary planning problems. It uses the well-known $L^2$ norm (i.e., Euclidean distance) to analyze minimum-path-length problems and shows that existing approaches decrease in effectiveness factorially (i.e., faster than exponentially) with state dimension. It presents a method to address this curse of dimensionality by directly sampling the prolate hyperspheroids (i.e., symmetric $n$-dimensional ellipses) that define the $L^2$ informed set. The importance of this direct informed sampling technique is demonstrated with Informed RRT*. This extension of RRT* has less theoretical dependence on state dimension and problem size than existing techniques and allows for linear convergence on some problems. It is shown experimentally to find better solutions faster than existing techniques on both abstract planning problems and HERB, a two-arm manipulation robot.