A rendering regression is a bug introduced by a web browser where a web page no longer functions as users expect. Such rendering bugs critically harm the usability of web browsers as well as web applications. The unique aspect of rendering bugs is that they affect the presented visual appearance of web pages, but those web pages have no pre-defined correct appearance. Therefore, it is challenging to automatically detect errors in their appearance. In practice, web browser vendors rely on non-trivial and time-prohibitive manual analysis to detect and handle rendering regressions.

This paper describes our successful implementation of a robot that autonomously and strategically removes multiple blocks from an unstable Jenga tower. We present an integrated strategy for perception, planning and control that achieves repeatable performance in this challenging physical domain. In contrast to previous implementations, we rely only on low-cost, readily available system components and use strategic algorithms to resolve system uncertainty. We present a three-stage planner for block extraction which considers block selection, extraction order, and physics-based simulation that evaluates removability. Existing vision techniques are combined in a novel sequence for the identification and tracking of blocks within the tower. Discussion of our approach is presented following experimental results on a 5-DOF robot manipulator.