CLAMP: A Sim-to-Real Benchmark for Closed-Loop Kinematic Pose Estimation and Assembly Reasoning

Existing benchmarks for known-object pose estimation either treat objects as rigid bodies or restrict articulation to serial-chain robot arms with revolute joints, and all assume that every part is always present. Real-world industrial and consumer equipment, however, exhibits closed-loop kinematic chains, prismatic joints, and structural assembly perturbations—challenges that current datasets and methods do not address. We introduce CLAMP (Closed-Loop Assembly and Mechanism Perception), a sim-to-real benchmark for kinematic pose estimation and assembly reasoning on mechanically complex known objects. The dataset spans seven diverse pieces of equipment—ranging from a delta 3D printer with 27 coupled DOFs to a hydraulic jack with closed kinematic loops—comprising over two million synthetic training images and approximately 21,000 real test images across 210 scenes, each annotated with ground-truth joint states and part-level assembly labels. To produce this data we develop (i) a graph-based kinematic representation that extends beyond URDF's tree structure to support closed-loop constraints and dynamic topology changes induced by missing parts; (ii) a synthetic data generator that samples valid poses on the kinematic constraint manifold and stochastically removes parts with automatic kinematic restructuring; and (iii) a real-image labeling pipeline that jointly recovers camera alignment and equipment pose via multi-view constrained optimization. Baseline evaluation with RoboPEPP—the current state of the art on the DREAM robot pose benchmark—shows that our constrained pose sampling is critical for closed-loop equipment, yet performance on the combined task of kinematic estimation under assembly perturbation remains low, highlighting an open challenge for the community. We release the full dataset, the synthetic data generation pipeline, evaluation code, and baseline implementation.