Sim-to-Real-Transfer-Risk-Dataset-Robotics-RL

This dataset simulates the relationship between simulation-training choices and real-world deployment outcomes for RL policies, across 750 configurations spanning five India-relevant robotics tasks (agricultural drone spraying, warehouse navigation, contact-rich manipulation, quadruped locomotion, low-speed autonomous e-rickshaw maneuvering), five randomization methods, parallel-environment counts from 64 to 16384, and offline pretraining choices. Every row is a simulation-phase observable; the real-world deployment label is never directly observable during simulation, by construction. Ten grounding sources span 2017 to 2024, documented in sim2real_theory.py and the README Research basis.