In order for robots to work alongside humans in a range of domains, they will need to operate with a variety of social dynamics that each context will require. This paper builds on previous work with a parameterized turn-taking model, CADENCE, in which different parameter settings resulted in different social dynamics. In contrast to the static parameter settings of previous work, we now investigate the problem of changing these turn-taking parameter sets dynamically within a single interaction session. This ability is necessary for successful peer-to-peer collaborations, in which balance of control between leading and following must be maintained. We present our dynamic switching approach and an experiment with 15 participants. Our results confirm that it is possible to achieve the same changes in social dynamics within a single interaction session that were previously seen only between independent sessions of different parameter settings. Moreover, we show that such a change in social dynamics is contingent upon changing parameters at socially appropriate turn boundaries.

Timing plays a role in a range of human-robot interaction scenarios, as humans are highly sensitive to timing and interaction fluency. It is central to spoken dialogue, with turn-taking, interruptions, and hesitation influencing both task efficiency and user affect. Timing is also an important factor in the interpretation and generation of gestures, gaze, facial expressions, and other nonverbal behavior. Beyond communication, temporal synchronization is functionally necessary for sharing resources and physical space, as well as coordinating multi-agent actions. Timing is thus crucial to the success of a broad spectrum of HRI applications, including but not limited to situated dialogue; collaborative manipulation; performance, musical, and entertainment robots; and expressive robot companions. Recent years have seen a growing interest in the HRI community in the various research topics related to human-robot timing. The purpose of this workshop is to explore and discuss theories, computational models, systems, empirical studies, and interdisciplinary insights related to the notion of timing, fluency, and rhythm in human-robot interaction.

Turn-taking is ubiquitous in human communication, yet turn-taking between humans and robots continues to be stilted and awkward for human users. The goal of our work is to build autonomous robot controllers for successfully engaging in human-like turn-taking interactions. Towards this end, we present CADENCE, a novel computational model and architecture that explicitly reasons about the four components of floor regulation: seizing the floor, yielding the floor, holding the floor, and auditing the owner of the floor. The model is parametrized to enable the robot to achieve a range of social dynamics for the human-robot dyad. In a between-groups experiment with 30 participants, our humanoid robot uses this turn-taking system at two contrasting parametrizations to engage users in autonomous object play interactions. Our results from the study show that: (1) manipulating these turn-taking parameters results in significantly different robot behavior; (2) people perceive the robot's behavioral differences and consequently attribute different personalities to the robot; and (3) changing the robot's personality results in different behavior from the human, manipulating the social dynamics of the dyad. We discuss the implications of this work for various contextual applications as well as the key limitations of the system to be addressed in future work.

In human cooperation, the concurrent usage of multiple social modalities such as speech, gesture, and gaze results in robust and efficient communicative acts. Such multimodality in combination with reciprocal intentions supports fluent turn-taking. I hypothesize that human-robot turn-taking can be made more fluent through appropriate timing of multimodal actions. Managing timing includes understanding the impact that timing can have on interactions as well as having a control system that supports the manipulation of such timing. To this end, I propose to develop a computational turn-taking model of the timing and information flow of reciprocal interactions. I also propose to develop an architecture based on the timed Petri net (TPN) for the generation of coordinated multimodal behavior, inside of which the turn-taking model will regulate turn timing and action initiation and interruption in order to seize and yield control. Through user studies in multiple domains, I intend to demonstrate the system's generality and evaluate the system on balance of control, fluency, and task effectiveness.

Turn-taking interactions with humans are multimodal and reciprocal in nature. In addition, the timing of actions is of great importance, as it influences both social and task strategies. To enable the precise control and analysis of timed discrete events for a robot, we develop a system for multimodal collaboration based on a timed Petri net (TPN) representation. We also argue for action interruptions in reciprocal interaction and describe its implementation within our system. Using the system, our autonomously operating humanoid robot Simon collaborates with humans through both speech and physical action to solve the Towers of Hanoi, during which the human and the robot take turns manipulating objects in a shared physical workspace. We hypothesize that action interruptions have a positive impact on turn-taking and evaluate this in the Towers of Hanoi domain through two experimental methods. One is a between-groups user study with 16 participants. The other is a simulation experiment using 200 simulated users of varying speed, initiative, compliance, and correctness. In these experiments, action interruptions are either present or absent in the system. Our collective results show that action interruptions lead to increased task efficiency through increased user initiative, improved interaction balance, and higher sense of fluency. In arriving at these results, we demonstrate how these evaluation methods can be highly complementary in the analysis of interaction dynamics.

Turn-taking is fundamental to the way humans engage in information exchange, but robots currently lack the turn-taking skills required for natural communication. In order to bring effective turn-taking to robots, we must first understand the underlying processes in the context of what is possible to implement. We describe a data collection experiment with an interaction format inspired by “Simon says,” a turn-taking imitation game that engages the channels of gaze, speech, and motion. We analyze data from 23 human subjects interacting with a humanoid social robot and propose the principle of minimum necessary information (MNI) as a factor in determining the timing of the human response.We also describe the other observed phenomena of channel exclusion, efficiency, and adaptation. We discuss the implications of these principles and propose some ways to incorporate our findings into a computational model of turn-taking.

This research aims to enable robots to learn from human teachers. Motivated by human social learning, we believe that a transparent learning process can help guide the human teacher to provide the most informative instruction. We believe active learning is an inherently transparent machine learning approach because the learner formulates queries to the oracle that reveal information about areas of uncertainty in the underlying model. In this work, we implement active learning on the Simon robot in the form of nonverbal gestures that query a human teacher about a demonstration within the context of a social dialogue. Our preliminary pilot study data show potential for transparency through active learning to improve the accuracy and efficiency of the teaching process. However, our data also seem to indicate possible undesirable effects from the human teacher's perspective regarding balance of the interaction. These preliminary results argue for control strategies that balance leading and following during a social learning interaction.

Spatial scaffolding is a naturally occurring human teaching behavior, in which teachers use their bodies to spatially structure the learning environment to direct the attention of the learner. Robotic systems can take advantage of simple, highly reliable spatial scaffolding cues to learn from human teachers. We present an integrated robotic architecture that combines social attention and machine learning components to learn tasks effectively from natural spatial scaffolding interactions with human teachers. We evaluate the performance of this architecture via a human subjects experiment which examines our humanoid robotpsilas ability to learn from live interactions with human teachers in a secret-constraint task domain. This evaluation provides quantitative evidence for the utility of spatial scaffolding cues to systems that learn from natural human teaching behavior.