Robotics benchmarking: where we are, and what comes next

Robotics benchmarks have evolved from shared object sets such as YCB into diverse evaluations of perception, grasping, task execution, language following, robustness, lifelong learning, navigation, humanoid control, and real-world manipulation. Simulation benchmarks including MetaWorld, RLBench, ManiSkill, LIBERO, CALVIN, and RoboCasa365 enable repeatable testing but reveal that combining learned skills and generalizing to new instructions or conditions remains substantially harder than isolated tasks, while physical benchmarks capture hardware-specific challenges such as sensing, contact, calibration, and object variation that simulation cannot fully establish. Reproducing real robot experiments is costly because setups, sensors, objects, and trial conditions differ, requiring repeated tests, careful documentation, standardized metadata, and sustained infrastructure investment. The article argues that future benchmarks should not only measure capability but also identify recurring failures and guide decisions about what training data to collect, such as recovery demonstrations for failed insertions. ProjectSim’s initial work investigates whether higher-fidelity simulated reconstructions of real scenes, including geometry, appearance, physics, and visual backgrounds, can better match physical robot performance, potentially enabling scalable simulated practice that can be validated on real hardware.

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