Gopika Ajaykumar

Amama Mahmood, Gopika Ajaykumar, Chien-Ming Huang

Artificial Intelligence (AI) is an integral part of our daily technology use and will likely be a critical component of emerging technologies. However, negative user preconceptions may hinder adoption of AI-based decision making. Prior work has highlighted the potential of factors such as transparency and explainability in improving user perceptions of AI. We further contribute to work on improving user perceptions of AI by demonstrating that bringing the user in the loop through mock model training can improve their perceptions of an AI agent's capability and their comfort with the possibility of using technology employing the AI agent.

Gopika Ajaykumar, Annie Mao, Jeremy Brown et al.

Robots are envisioned to work alongside humans in applications ranging from in-home assistance to collaborative manufacturing. Research on human-robot collaboration (HRC) has helped develop various aspects of social intelligence necessary for robots to participate in effective, fluid collaborations with humans. However, HRC research has focused on dyadic, structured, and minimal collaborations between humans and robots that may not fully represent the large scale and emergent nature of more complex, unstructured collaborative activities. Thus, there remains a need for shared testbeds, datasets, and evaluation metrics that researchers can use to better model natural, ad-hoc human collaborative behaviors and develop robot capabilities intended for large scale emergent collaborations. We present one such shared resource - FACT (Full-body Ad-hoc Collaboration Testbed), an openly accessible testbed for researchers to obtain an expansive view of the individual and team-based behaviors involved in complex, co-located teamwork. We detail observations from a preliminary exploration with teams of various sizes and discuss potential research questions that may be investigated using the testbed. Our goal is for FACT to be an initial resource that supports a more holistic investigation of human-robot collaboration.

Gopika Ajaykumar, Maureen Steele, Chien-Ming Huang

As robots interact with a broader range of end-users, end-user robot programming has helped democratize robot programming by empowering end-users who may not have experience in robot programming to customize robots to meet their individual contextual needs. This article surveys work on end-user robot programming, with a focus on end-user program specification. It describes the primary domains, programming phases, and design choices represented by the end-user robot programming literature. The survey concludes by highlighting open directions for further investigation to enhance and widen the reach of end-user robot programming systems.