Large Language Models (LLMs) have the potential to catalyze a paradigm shift in end-user robot programming---moving from the conventional process of user specifying programming logic to an iterative, collaborative process in which the user specifies desired program outcomes while LLM produces detailed specifications. We introduce a novel integrated development system, Alchemist, that leverages LLMs to empower end-users in creating, testing, and running robot programs using natural language inputs, aiming to reduce the required knowledge for developing robot applications. We present a detailed examination of our system design and provide an exploratory study involving true end-users to assess capabilities, usability, and limitations of our system. Through the design, development, and evaluation of our system, we derive a set of lessons learned from the use of LLMs in robot programming. We discuss how LLMs may be the next frontier for democratizing end-user development of robot applications.

Robotic systems designed to work alongside people are susceptible to technical and unexpected errors. Prior work has investigated a variety of strategies aimed at repairing people's trust in the robot after its erroneous operations. In this work, we explore the effect of post-error trust repair strategies (promise and explanation) on people's trust in the robot under varying power dynamics (supervisor and subordinate robot). Our results show that, regardless of the power dynamics, promise is more effective at repairing user trust than explanation. Moreover, people found a supervisor robot with verbal trust repair to be more trustworthy than a subordinate robot with verbal trust repair. Our results further reveal that people are prone to complying with the supervisor robot even if it is wrong. We discuss the ethical concerns in the use of supervisor robot and potential interventions to prevent improper compliance in users for more productive human-robot collaboration.