Safe and Robust Neural Policy Learning with Statistical Verification for Sim-to-Real Deployment in Robotics
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arXiv:2608. 06481v1 Announce Type: cross Abstract: Training controllers that are safe and robust in simulation, and systematically assessing their readiness for real-world deployment, remain key challenges in sim-to-real transfer.
arXiv:2607. 15003v1 Announce Type: new Abstract: The deployment of autonomous cyber-physical systems in safety-critical environments requires closed-loop control strategies (i.
arXiv:2608. 19425v1 Announce Type: cross Abstract: Reliable performance evaluation is a central bottleneck for deploying robot-learning policies in real-world conditions.
arXiv:2607. 07252v1 Announce Type: new Abstract: Reinforcement learning (RL) enables the synthesis of control policies directly from data, making it highly appealing for complex cyber-physical systems (CPSs) and robotics.
SCAPE is a scenario‑conditioned simulation‑augmented policy evaluation framework that predicts real‑world policy performance for specific scenarios using limited paired simulation‑and‑real samples and extensive simulation rollouts. It corrects sim‑to‑real bias in simulation labels before training the prediction model and calibrates prediction uncertainty via conformal prediction. Experiments on autonomous driving and quadruped velocity tracking show SCAPE reduces scenario‑level prediction error, improves testing sample efficiency, narrows calibrated prediction intervals, and generalizes better to out‑of‑distribution scenarios, enabling fine‑grained deployment strategies.
Reinforcement learning (RL) enables the synthesis of control policies directly from data, making it highly appealing for complex cyber-physical systems (CPSs) and robotics. A persistent challenge, however, is ensuring strict, hard safety constraints during the active learning phase.