RoboValue: A Fine-Grained Sim-and-Real Benchmark for Unified Evaluation of Robotic Value Models

General-purpose robotic value models detect task-state changes from visual observations and instructions to score task progress across tasks and embodiments. Yet existing benchmarks test only episode outcomes or isolated process abilities, through model-specific interfaces and mostly on clean successful or failed rollouts, and thus cannot tell whether a model tracks task state or exploits shortcuts such as elapsed time and visual similarity. We introduce RoboValue, a benchmark with a unified protocol for fine-grained evaluation of robotic value models on simulation and real-world tasks. Shared interfaces and adapters enable comparison across heterogeneous models while preserving native value semantics. RoboValue evaluates four complementary dimensions: task-state understanding, temporal progress monitoring, failure and recovery reasoning, and value consistency. Beyond common successful and failed rollouts, it includes diagnostic trajectories with incomplete subtasks, effective and ineffective recovery, visually similar states with different execution histories, and multiple valid action orders. We evaluate 15 model variants from 9 families in zero-shot and one-shot settings, assessing performance under standard conditions and generalization across embodiment and environment shifts. RoboValue provides a public leaderboard and a diagnostic foundation for developing more reliable and general robotic value models. We propose RoboValue-Dataset, which comprises an evaluation set of 2,792 trajectories spanning 15 simulation and 20 real-world manipulation tasks across 4 embodiments, with corresponding training data. The website is available at https://robovalue.github.io/.