TOWER: Benchmarking Bimanual Manipulation across Simulation and Reality

Complex robotic manipulation requires interpreting visual scenes and anticipating how actions change object contacts and structural support. Evaluating these capabilities calls for diverse, long-horizon tasks in which the physical consequences of earlier actions shape subsequent execution. To this end, we introduce TOWER, a novel Jenga-based benchmark for sustained contact and structural change, comprising six configurable task families involving bimanual coordination and tool use. Jenga provides repeatable structures in which local manipulations alter the support and stability needed for subsequent actions. A Real2Sim pipeline builds corresponding simulated environments for scalable evaluation. We evaluate four policy architectures across twelve configurations using separately trained real and simulated policies. Progress metrics reveal substantial partial completion hidden by binary task success, while taller configurations generally exhibit lower progress. Real–sim evaluation shows strong progress correlation (Pearson r = 0.880) and a normalized MMRV of 0.1217. TOWER provides a unified testbed for studying how policies respond to interaction-induced scene changes and supports systematic policy evaluation across reality and simulation. Code is available at https://anonymous.4open.science/r/artifact-3d0e01332a1edd93.