RecoverBench: A Systematic Benchmark for Error Recovery in Robotic Manipulation

Robotic manipulation policies have achieved remarkable success in controlled settings, but they catastrophically fail when execution errors occur—dropped objects, collisions, misaligned grasps. Despite the critical importance of error recovery for real-world deployment, no existing benchmark systematically evaluates manipulation policies' ability to recover from such errors. We introduce RecoverBench, the first systematic benchmark for error recovery in robotic manipulation. We propose a taxonomy of 12 Error Skills organized into 5 Recovery Behavior Groups (RBGs), spanning 24 error subtypes across 2 difficulty degrees. Our Error Skill pipeline automatically generates 1,360 reproducible error scenes from clean demonstration trajectories across 6 manipulation tasks. Each error scene includes complete simulation state snapshots, environment fingerprints, and RNG states for deterministic reproduction. Beyond evaluation, RecoverBench also supports recovery-demo collection and augmentation, and we show that this recovery supervision improves recovery performance. We evaluate 4 representative manipulation policies on RecoverBench. Even the best-performing policy reaches only 48.6% average recovery success, ranging from 32.0% on threading to 76.7% on stack, showing that current methods still lack robust recovery. These results highlight the need for dedicated recovery evaluation and position RecoverBench as a benchmark for improving recovery robustness in manipulation policies. Code, data, and evaluation protocols will be released.