Digital Robot Judge: Building a Task-Centric Performance Database of Real-World Manipulation With Electronic Task Boards
Peter So, Andriy Sarabakha, Fan Wu, Utku Çulha, Fares J. Abu‐Dakka, Sami Haddadin
Abstract
Robotics aims to develop manipulation skills approaching human performance. However, skill complexity is often over- or underestimated based on individual experience, and the real-world performance gap is difficult or expensive to measure through in-person competitions. To bridge this gap, we propose a compact, Internet-connected, electronic task board to measure manipulation performance remotely; we call it the digital robot judge, or “DR.J.” By detecting key events on the board through performance circuitry, DR.J provides an alternative to transporting equipment to in-person competitions and serves as a portable test and data-generation system that captures and grades performances, making comparisons less expensive. Data collected are automatically published on a web dashboard (WD) that provides a living performance benchmark that can visualize improvements in real-world manipulation skills of robot platforms over time across the globe.