Provably Safe Sim-to-Real Transfer
To mitigate the sample complexity of real-world reinforcement learning (RL), a common practice is to first train a policy in a simulator, where samples are cheap, and then deploy the learned policy in the real world with the hope that it generalizes effectively. Such direct sim-to-real transfer is not guaranteed to succeed: simulator-trained policies can be suboptimal in the real world due to sim-to-real mismatch. Correcting this mismatch requires collecting data from the real system, but in many applications, such as robotics and healthcare, this data-collection process is itself subject to s
Lineage graph
Paper → model → repo connections mined from source citations (Tier-1 exact match).
Why these links exist
Every edge carries a method, confidence, and the source snippet that justified it — so bad links are debuggable.
- PossiblePossibly related (embedding) · 50%The State of Simulation for Physical AI: An Overview →
- PossiblePossibly related (embedding) · 50%From virtual experiments to biomedical insight with synthetic data →
- PossiblePossibly related (embedding) · 49%GPT-Red: Unlocking Self-Improvement for Robustness →
- LinkedLinked via arxiv author · 85%Tingting Ni →
“Provably Safe Sim-to-Real Transfer”
- LinkedLinked via arxiv author · 85%Maryam Kamgarpour →
“Provably Safe Sim-to-Real Transfer”