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, an illegibility measure for trajectories, and a novel motion planner based on potential fields. Both our measures 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 that our proposed legible motion planner leads to a marginally significant increase in legibility compared to the current state-of-the-art legible motion planner. 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.

AI Institute for Transforming Education for Children with Speech and Language Processing Challenges, National Science Foundation PI: Venugopal Govindaraju, co-PI: David Feil-Seifer, Amount: $20,000,000, Jan. 15, 2023 - Dec. 31, 2027