Overview
- Developed at TUM’s Learning Systems and Robotics Lab, the mobile platform builds centimeter-accurate room maps by combining camera images with depth data.
- An active update mode prioritizes rescanning surfaces that often change, such as tables and countertops, while de-emphasizing largely static areas.
- A large language model provides commonsense relations about objects and interprets voice queries so the robot can target the most probable locations first.
- In controlled evaluations, the prototype found objects 14% faster and searched about 30% more efficiently, with a reported 95% accuracy for detecting environmental changes.
- The work by Benjamin Bogenberger et al. is documented in IEEE Robotics and Automation Letters (DOI: 10.1109/LRA.2026.3656790), and the team aims to add manipulators to open drawers and doors for hidden-item retrieval.