Elena Corina Grigore

How to train your DragonBot: Socially assistive robots for teaching children about nutrition through play

This paper describes an extended (6-session) interaction between an ethnically and geographically diverse group of 26 first-grade children and the DragonBot robot in the context of learning about healthy food choices. We find that children demonstrate a high level of enjoyment when interacting with the robot, and a statistically significant increase in engagement with the system over the duration of the interaction. We also find evidence of relationship-building between the child and robot, and encouraging trends towards child learning. These results are promising for the use of socially assistive robotic technologies for long-term one-on-one educational interventions for younger children.

Talk to Me: Verbal Communication Improves Perceptions of Friendship and Social Presence in Human-Robot Interaction

The ability of social agents, be it virtually-embodied avatars or physically-embodied robots, to display social behavior and interact with their users in a natural way represents an important factor in how effective such agents are during interactions. In particular, endowing the agent with effective communicative abilities, well-suited for the target application or task, can make a significant difference in how users perceive the agent, especially when the agent needs to interact in complex social environments. In this work, we consider how two core input communication modalities present in human-robot interaction—speech recognition and touch-based selection—shape users’ perceptions of the agent. We design a short interaction in order to gauge adolescents’ reaction to the input communication modality employed by a robot intended as a long-term companion for motivating them to engage in daily physical activity. A study with n = 52 participants shows that adolescents perceive the robot as more of a friend and more socially present in the speech recognition condition than in the touch-based selection one. Our results highlight the advantages of using speech recognition as an input communication modality even when this represents the less robust choice, and the importance of investigating how to best do so.

Comparing Ways to Trigger Migration Between a Robot and a Virtually Embodied Character

The question of whether to use a robot or a virtually-embodied character for applications in need of a socially intelligent agent depends on the requirements of the task at hand. To overcome limitations of both types of embodiment and benefit from advantages provided by both, we can complement a physical robot with a virtual counterpart. In order to link the two embodiments such that users perceive they are interacting with the same entity, the concept of “migration” from one embodiment to the other needs to be addressed. In this work, we investigate a particular aspect of this concept, namely how to best perform the triggering of migration, within the context of a physical activity motivation scenario for adolescents. We design two methods, a proximity-based method and a control, and compare their effects on adolescents’ perceptions of our agent. Results show that users perceive the agent as more of a friend and more socially present in the proximity-based than in the control condition. This emphasizes the importance of investigating different facets of entity migration for systems in need of employing both a physical and virtual embodiment for an artificial agent.

Discovering Action Primitive Granularity from Human Motion for Human-Robot Collaboration

Developing robots capable of making sense of their environment requires the ability to learn from observations. An important paradigm that allows for robots to both imitate humans and gain an understanding of the tasks people perform is that of action primitive discovery. Action primitives have been used as a representation of the main building blocks that compose motion. Automatic primitive discovery is an active area of research, with existing methods that can provide viable solutions for learning primitives from demonstrations. However, when we learn primitives directly from raw data, we need a mechanism to determine those primitives that are appropriate for the task at hand: is brushing one’s teeth a suitable primitive or are the actions of grabbing the toothbrush, adding toothpaste onto it, and executing the brushing motion better suited? It is this level of granularity that is important for determining well-suited primitives for applications. Existing methods for learning primitives do not provide a solution for discovering their granularity. Rather, these techniques stop at arbitrarily chosen levels, and often use clear, repetitive actions in order to easily label the primitives. Our contribution provides a framework for discovering the appropriate granularity level of learned primitives for a task. We apply our framework to action primitives learned from a set of motion capture data obtained from human demonstrations that includes hand and object motions. This helps find a wellsuited granularity level for our task, avoiding the use of low levels that don’t capture the necessary core pattern in the actions, or high levels that miss important differences between actions. Our results show that this framework is able to discover the best suited primitive granularity level for a specific application.

Constructing Policies for Supportive Behaviors and Communicative Actions in Human-Robot Teaming

Current state-of-the-art robotic systems deployed in industry work in isolation from humans and do not allow for collaboration. Developing a robot that can work side-by-side with a human presents the advantage of allowing both the robot and the human worker to focus on the task each is best suited for, while assisting one another as needed. For the robot to provide assistive behavior to a human co-worker, it needs to learn what actions it should perform at each time step depending upon the state of the task. Such assistive actions are not intended to simply contribute to the completion of a particular task by instructing the robot to work on subtasks in isolation from the human worker; rather they are meant to help the worker complete the task more efficiently. As such, employing standard policy search or task and motion planning techniques is not sufficient to discover the supportive types of actions my system seeks to offer based on accurate estimations of the current task state. To this end, my research focuses on investigating policy search within hierarchical tasks that allow for two main abilities, namely helping the human co-worker more effectively complete a task and taking communicative actions that reduce state estimation uncertainty by asking the worker direct questions. The policy dictates what action the robot should take at each time step, based on inputs from a motion capture system providing observations about the configuration of the persons hands relative to the objects needed for accomplishing the task, as well as the persons answers to any questions posed by the robot.