While robotics and game development have independently achieved significant progress in creating interactive and intelligent systems, a deeper collaboration between these fields could be mutually beneficial. This paper argues for more collaboration, highlighting current limited interactions and proposing directions for future research. We discuss shared foundations such as Artificial Intelligence, Extended Reality, and the increasing use of common tools and standards. We then propose opportunities where game development methodologies can advance robotics (e.g., gamified data collection and richer simulation environments) and where robotics research can contribute to games (e.g., improved NPC autonomy and embodied intelligence). This cross-disciplinary interaction can accelerate innovation and lead to more intelligent and usercentered technologies in both domains.

In this paper, we aim to explore how humans give unstructured free-form natural language feedback towards correcting robot task policies. We present a qualitative study based on crowd-sourced feedback from 66 participants. Participants give feedback on the execution of three robotic tasks in the form of mobile navigation, dexterous object manipulation and a robot arm opening a door. We find through reflexive thematic analysis which features participants reference most and what other patterns are present in the feedback, including that responders do not naturally give concrete and actionable feedback. We attribute the lack of feedback concreteness to a lack of engagement with robot behavior and false assumptions about who is receiving the feedback and how much knowledge participants have. The study presents a step towards better understanding unstructured language feedback for robotic learning.