PLANNINGCURRENT2026-05-18

Bench2Drive-Robust: Benchmarking Closed-Loop Autonomous Driving under Deployment Perturbations

Zhiyuan Zhang, Zhenghao Jin, Yanlun Peng, Xianda Guo, Haoran Liu, Shaofeng Zhang, Xingjun Ma, Zuxuan Wu, Junchi Yan, Xiaosong Jia, Yu-Gang Jiang

This Simulation & Sim-to-RealBenchmarkA standard test used to compare methods fairly. reveals that real-world Simulation & Sim-to-RealDeploymentPutting the trained system on a real robot. issues—frame drops, GPS Data, Distributions & Training IssuesNoiseUnwanted variation or randomness in sensor readings or actuation., Robot LearningInferenceUsing a trained model to make predictions or choose actions. delays—break end-to-end driving systems far more than synthetic image corruptions alone. If you're shipping an autonomous driving stack, you now have a systematic way to test whether Simulation & Sim-to-RealLatencyDelay between input, computation, and action. and Perception & SensingSensorA device that provides information about the robot or its environment. errors will crash your vehicle.

ARCHITECTURE

THE PROBLEM

This paper focuses on Control & PlanningPlanningFiguring out what the robot should do before or during movement.. First closed-loop Modern Robot LearningRobustnessHow well a robot keeps working despite noise, disturbances, or variation. Simulation & Sim-to-RealBenchmarkA standard test used to compare methods fairly. for end-to-end autonomous driving that evaluates system-level Simulation & Sim-to-RealDeploymentPutting the trained system on a real robot. perturbations (frame drops, GPS/odometry errors, Robot LearningInferenceUsing a trained model to make predictions or choose actions. delay) rather than just image-level corruptions. Shows these real-world imperfections accumulate through the Control & PlanningFeedbackInformation returned from sensors during action to help correct behavior. loop and substantially degrade driving performance. Read the paper by tracking the Core ConceptsTaskThe job the robot is supposed to complete, such as pick-and-place, navigation, or drawer opening. definition, the Core ConceptsRobotA physical system with sensors and actuators that can observe the world and take actions. or data assumptions, and the evidence that supports the claimed improvement.

HOW IT WORKS

1

Task framing

The paper frames the work as Control & PlanningPlanningFiguring out what the robot should do before or during movement.. Start here because it defines what success means and which assumptions the rest of the method inherits.

2

Core method

This Simulation & Sim-to-RealBenchmarkA standard test used to compare methods fairly. reveals that real-world Simulation & Sim-to-RealDeploymentPutting the trained system on a real robot. issues—frame drops, GPS Data, Distributions & Training IssuesNoiseUnwanted variation or randomness in sensor readings or actuation., Robot LearningInferenceUsing a trained model to make predictions or choose actions. delays—break end-to-end driving systems far more than synthetic image corruptions alone. If you're shipping an autonomous driving stack, you now have a systematic way to test whether Simulation & Sim-to-RealLatencyDelay between input, computation, and action. and Perception & SensingSensorA device that provides information about the robot or its environment. errors will crash your vehicle. When reading the method section, identify the inputs, the learned or engineered representation, and the Core ConceptsActionA command the robot sends to its motors, controller, or low-level system. or prediction produced by the system.

3

Data and supervision

For robotics work, the data story is part of the method: check whether the system depends on Imitation & Reinforcement LearningTeleoperation (teleop)A human remotely controlling the robot, often to collect demonstrations., Simulation & Sim-to-RealSimulationA virtual environment where robots can be trained or tested., internet video, human labels, or Core ConceptsRobotA physical system with sensors and actuators that can observe the world and take actions. rollouts.

4

Evaluation evidence

The paper should be judged through its Simulation & Sim-to-RealEvaluationMeasuring how well a robot system performs. protocol: what data is used, what Core ConceptsRobotA physical system with sensors and actuators that can observe the world and take actions. or simulator is tested, and which Evaluation & ResearchBaselineA reference method used for comparison. comparisons support the claim. Look for the gap between the headline result and the Simulation & Sim-to-RealDeploymentPutting the trained system on a real robot. setting you would actually care about.

FIGURES

KEY RESULTS

Main contributionConceptual contribution

This Simulation & Sim-to-RealBenchmarkA standard test used to compare methods fairly. reveals that real-world Simulation & Sim-to-RealDeploymentPutting the trained system on a real robot. issues—frame drops, GPS Data, Distributions & Training IssuesNoiseUnwanted variation or randomness in sensor readings or actuation., Robot LearningInferenceUsing a trained model to make predictions or choose actions. delays—break end-to-end driving systems far more than synthetic image corruptions alone. If you're shipping an autonomous driving stack, you now have a systematic way to test whether Simulation & Sim-to-RealLatencyDelay between input, computation, and action. and Perception & SensingSensorA device that provides information about the robot or its environment. errors will crash your vehicle.

WHY DEVELOPERS SHOULD CARE

This Simulation & Sim-to-RealBenchmarkA standard test used to compare methods fairly. reveals that real-world Simulation & Sim-to-RealDeploymentPutting the trained system on a real robot. issues—frame drops, GPS Data, Distributions & Training IssuesNoiseUnwanted variation or randomness in sensor readings or actuation., Robot LearningInferenceUsing a trained model to make predictions or choose actions. delays—break end-to-end driving systems far more than synthetic image corruptions alone. If you're shipping an autonomous driving stack, you now have a systematic way to test whether Simulation & Sim-to-RealLatencyDelay between input, computation, and action. and Perception & SensingSensorA device that provides information about the robot or its environment. errors will crash your vehicle.

LIMITATIONS

The main limitation to check is whether the claimed behavior holds outside the paper's reported setup. That means testing across different Core ConceptsRobotA physical system with sensors and actuators that can observe the world and take actions. embodiments, scenes, objects, and data distributions.

WHAT COMES NEXT

The practical next step is independent reproduction with clear baselines, ablations, and stress tests. For a developer, the useful follow-up is to map the paper's Control & PlanningPlanningFiguring out what the robot should do before or during movement. assumptions onto a concrete Core ConceptsRobotA physical system with sensors and actuators that can observe the world and take actions. stack, then test the smallest version of the method that could run end to end.

RELATED PAPERS