Robot morphology-the form, body shape, and structure of robots-makes up a key design space in human-robot interaction (HRI), shaping how robots function, express themselves, and interact with humans. Yet, despite its importance, little is known about how design frameworks might guide form exploration and generation. To address this gap, we introduce Elements of Robot Morphology, a design framework that identifies five fundamental elements: intelligence, kinematics, end effectors, locomotion, and structure. Based on an analysis of robots in the IEEE robot database, this framework provides a foundation for exploring diverse robot forms. To operationalize the framework, we developed Morphology Exploration Blocks (MEB), a set of tangible blocks that enable designers to physically build and experiment with different morphologies, fostering hands-on and collaborative exploration. We evaluated the framework and toolkit through a case study and a series of design workshops, demonstrating their support for analysis, ideation, reflection, and collaborative creation in robot design.

Roboticists are continually improving the quality of social robot behaviors and interactions with humans. This is a major goal of the field of social robotics, which seeks to create socially competent robots and improve their overall acceptance. Toward this effort, we propose the utilization of primal world beliefs (i.e., beliefs about the character of the world) to design behaviors that are relatable, intuitive, and based on an internal motivation. However, it is not yet clear whether humans can reliably discern these world beliefs in robots. In this work, we explore whether primals can serve as a novel framework to inform the design of robot personality and attempt to understand whether and how humans perceive primals within robots. Through two large online user studies ( \(n=300\) ; \(n=360\) ), we show that (1) participants are broadly able to discern intended primals in robots, (2) certain participant and robot primals predict participant perception of the robots, and (3) similarity between human and robot primals predicts improved perception of robots.

While robots possess many capabilities that may positively influence human lives, their autonomous navigation and sensing capabilities pose threats to user privacy. These threats may be addressed at three key phases: data collection, data retention, and data exposure. In this work, we discuss our prior, current, and proposed robot design efforts to reduce privacy violations during human-robot interaction (HRI). At the data collection phase, we are currently exploring designs that enable robots to inhibit data collection by blocking their own sensors. At the data retention phase, we propose the exploration of privacy preferences to inform designs that grant users greater control over retained data. Finally, in the data exposure phase, we discuss our prior works developing a privacy controller for appropriate data exposure and generating task-planning strategies to limit unintentional data exposure. Through this work, we hope to protect user data and reduce the likelihood of harm to users.

Robots are ubiquitous in small-to-large-scale manufacturers. While collaborative robots (cobots) have significant potential in these settings due to their flexibility and ease of use, proper integration is critical to realize their full potential. Specifically, cobots need to be integrated in ways that utilize their strengths, improve manufacturing performance, and facilitate use in concert with human workers. Effective integration requires careful consideration and the knowledge of roboticists, manufacturing engineers, and business administrators. We propose an approach involving the stages of planning, analysis, development, and presentation, to inform manufacturers about cobot integration within their facilities prior to the integration process. We contextualize our approach in a case study with an SME collaborator and discuss insights learned.

Robots designed to interact with people in collaborative or social scenarios must move in ways that are consistent with the robot's task and communication goals. However, combining these goals in a naïve manner can result in mutually exclusive solutions, or infeasible or problematic states and actions. In this paper, we present Lively, a framework which supports configurable, real-time, task-based and communicative or socially-expressive motion for collaborative and social robotics across multiple levels of programmatic accessibility. Lively supports a wide range of control methods (i.e. position, orientation, and joint-space goals), and balances them with complex procedural behaviors for natural, lifelike motion that are effective in collaborative and social contexts. We discuss the design of three levels of programmatic accessibility of Lively, including a graphical user interface for visual design called LivelyStudio, the core library Lively for full access to its capabilities for developers, and an extensible architecture for greater customizability and capability.

As social robots become increasingly prevalent in day-to-day environments, they will participate in conversations and appropriately manage the information shared with them. However, little is known about how robots might appropriately discern the sensitivity of information, which has major implications for human-robot trust. As a first step to address a part of this issue, we designed a privacy controller, Confidant, for conversational social robots, capable of using contextual metadata (e.g., sentiment, relationships, topic) from conversations to model privacy boundaries. Afterwards, we conducted two crowdsourced user studies. The first study ($n=174$) focused on whether a variety of human-human interaction scenarios were perceived as either private/sensitive or non-private/non-sensitive. The findings from our first study were used to generate association rules. Our second study ($n=95$) evaluated the effectiveness and accuracy of the privacy controller in human-robot interaction scenarios by comparing a robot that used our privacy controller against a baseline robot with no privacy controls. Our results demonstrate that the robot with the privacy controller outperforms the robot without the privacy controller in privacy-awareness, trustworthiness, and social-awareness. We conclude that the integration of privacy controllers in authentic human-robot conversations can allow for more trustworthy robots. This initial privacy controller will serve as a foundation for more complex solutions.