UniTeam: Open Vocabulary Mobile Manipulation Challenge
This work provides an incremental improvement for researchers in embodied AI working on mobile manipulation tasks in unfamiliar environments.
The authors tackled the HomeRobot Open Vocabulary Mobile Manipulation challenge by improving the baseline agent's performance through enhancements in perception, navigation, picking, and placing, resulting in reduced misclassifications, prevented infinite loops, and better object handling.
This report introduces our UniTeam agent - an improved baseline for the "HomeRobot: Open Vocabulary Mobile Manipulation" challenge. The challenge poses problems of navigation in unfamiliar environments, manipulation of novel objects, and recognition of open-vocabulary object classes. This challenge aims to facilitate cross-cutting research in embodied AI using recent advances in machine learning, computer vision, natural language, and robotics. In this work, we conducted an exhaustive evaluation of the provided baseline agent; identified deficiencies in perception, navigation, and manipulation skills; and improved the baseline agent's performance. Notably, enhancements were made in perception - minimizing misclassifications; navigation - preventing infinite loop commitments; picking - addressing failures due to changing object visibility; and placing - ensuring accurate positioning for successful object placement.