rlxp-dr-design

What problem does it solve?

This Skill helps you decide how to tune domain randomization when an RL policy performs well in nominal conditions but becomes brittle under randomized, held-out, or sim-to-real evaluation.

Core Features & Use Cases

• Robustness diagnosis: Compares nominal and randomized results to identify whether dynamics, observation, terrain, asset, latency, or actuation effects are causing the performance gap.
• Probe-backed tuning: Recommends controlled DR probes and uses their measured bounds to expand, contract, schedule, or rebalance randomization safely.
• Study-safe recommendations: Keeps evaluation distributions stable unless a new study is created, and avoids inventing broad ranges without evidence.
• Use case: A robot policy handles the training environment but fails on unseen surface friction, so the Skill proposes a targeted probe plan and a constrained DR update.

Quick Start

Use the rlxp-dr-design skill to review the current DR setup, compare robustness evidence, and propose a probe-backed tuning plan.