The environments in which the collaboration of a robot would be the most helpful to a person are frequently uncontrolled and cluttered with many objects present. Legible robot arm motion is crucial in tasks like these in order to avoid possible collisions, improve the workflow and help ensure the safety of the person. Prior work in this area, however, focuses on solutions that are tested only in uncluttered environments and there are not many results taken from cluttered environments. In this research we present a measure for clutteredness based on an entropic measure of the environment, and a novel motion planner based on potential fields. Both our measure and the planner were tested in a cluttered environment meant to represent a more typical tool-sorting task for which the person would collaborate with a robot. The in-person validation study with Baxter robots shows a significant improvement in legibility of our proposed legible motion planner compared to the current state-of-the-art legible motion planner in cluttered environments. Further, the results show a significant difference in the performance of the planners in cluttered and uncluttered environments, and the need to further explore legible motion in cluttered environments. We argue that the inconsistency of our results in cluttered environments with those obtained from uncluttered environments points out several important issues with the current research performed in the area of legible motion planners.

Facial expressiveness plays a crucial role in a robot’s ability to engage and interact with children. Prior research has shown that expressive robots can enhance child engagement during human-robot interactions. However, many robots used in therapy settings feature non-personalized, static faces designed with traditional facial feature considerations, which can limit the depth of interactions and emotional connections. Digital faces offer opportunities for personalization, yet the current landscape of robot face design lacks a dynamic, user-centered approach. Specifically, there is a significant research gap in designing robot faces based on child preferences. Instead, most robots in child-focused therapy spaces are developed from an adult-centric perspective. We present a novel study investigating the influence of child-drawn digital faces in child-robot interactions. This approach focuses on a design activity with children instructed to draw their own custom robot faces. We compare the perceptions of social intelligence (PSI) of two implementations: a generic digital face and a robot face, personalized using the user’s drawn robot faces. The results of this study show the perceived social intelligence of a child-drawn robot was significantly higher compared to a generic face.

In human-robot collaboration, legible intent of the robot is critical to success as it enables the human to more effectively work with and around the robot. Environments where humans and robots collaborate are widely varied and in the real world are most often cluttered. However, prior work in legible motion utilizes primarily environments which are uncluttered. Success in these environments does not necessarily guarantee success in more cluttered environments. Furthermore, the prior work has been primarily performed based on results from robot-human studies and the problem has not been studied from the prospective of what people do to express intent to each other. Therefore, this work addresses a gap in current research into legible robot arm motion in the following ways: first we perform a human-human study in order to establish the factors which humans use to express their intent through body language, and second we perform the study in a cluttered and varied environment. Through the study we showed that the primary factors which people considered are: timing, kinematic parameters, hand gestures, object proximity, etc. The results also showed that legibility is correlated with perceived safety, perceived social intelligence, the collaborator’s contribution, and trust which further speaks to the importance of legible motion. Future work will utilize the pose data extracted from the study’s video recordings to develop a model for legible motion.