How deep is AI's love? Understanding relational AI.

We suggest that as people move to construe robots as social agents, interact with them, and treat them as capable of social ties, they might develop (close) relationships with them. We then ask what kind of relationships can people form with bots, what functions can bots fulfill, and what are the societal and moral implications of such relationships.

Task-Oriented Active Sensing via Action Entropy Minimization.

In active sensing, sensing actions are typically chosen to minimize the uncertainty of the state according to some information-theoretic measure such as entropy, conditional entropy, mutual information, etc. This is reasonable for applications where the goal is to obtain information. However, when the information about the state is used to perform a task, minimizing state uncertainty may not lead to sensing actions that provide the information that is most useful to the task.

Training an Actor-Critic Reinforcement Learning Controller for Arm Movement Using Human-Generated Rewards.

Functional Electrical Stimulation (FES) employs neuroprostheses to apply electrical current to the nerves and muscles of individuals paralyzed by spinal cord injury to restore voluntary movement. Neuroprosthesis controllers calculate stimulation patterns to produce desired actions. To date, no existing controller is able to efficiently adapt its control strategy to the wide range of possible physiological arm characteristics, reaching movements, and user preferences that vary over time.

Application of the Actor-Critic Architecture to Functional Electrical Stimulation Control of a Human Arm.

Clinical tests have shown that the dynamics of a human arm, controlled using Functional Electrical Stimulation (FES), can vary significantly between and during trials. In this paper, we study the application of the actor-critic architecture, with neural networks for the both the actor and the critic, as a controller that can adapt to these changing dynamics of a human arm. Development and tests were done in simulation using a planar arm model and Hill-based muscle dynamics.

Creating a Reinforcement Learning Controller for Functional Electrical Stimulation of a Human Arm.

Clinical tests have shown that the dynamics of a human arm, controlled using Functional Electrical Stimulation (FES), can vary significantly between and during trials. In this paper, we study the application of Reinforcement Learning to create a controller that can adapt to these changing dynamics of a human arm. Development and tests were done in simulation using a two-dimensional arm model and Hill-based muscle dynamics.

Motion Planning Under Uncertainty for Image-guided Medical Needle Steering.

We develop a new motion planning algorithm for a variant of a Dubins car with binary left/right steering and apply it to steerable needles, a new class of flexible bevel-tip medical needles that physicians can steer through soft tissue to reach clinical targets inaccessible to traditional stiff needles. Our method explicitly considers uncertainty in needle motion due to patient differences and the difficulty in predicting needle/tissue interaction. The planner computes optimal steering actions to maximize the probability that the needle will reach the desired target.

Computationally efficient cardiac bioelectricity models toward whole-heart simulation.

This work studies the characteristics of excitable cell mathematical models, with the goal of developing new insights and techniques in simulating the electrical behavior of the human heart. While very simple models of such behavior can be simulated at real-time or better speeds on powerful computing equipment, the use of realistic cell models or organ-magnitude cell networks make the simulations computationally infeasible. We present an examination of the FitzHugh-Nagumo model and its response to stimulus and, in order to move toward the goal of a full cardiac simulation, we present a method of optimizing single-cell calculations through local interpolation techniques.