Exploring Unstructured Language Feedback for Robot Learning

Abstract

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.