Author: Jieyu Lian

Email: **jieyulian3@gmail.com**

1. Introduction: The Vision for Decentralized Robot Policy Reliability Audits

RoboProof is a Bittensor subnet for decentralized robot policy reliability audits and red-team testing. It addresses a concrete pre-deployment bottleneck in robotics: before a policy is tested on physical hardware, engineering teams need replayable evidence of where it fails, whether the failure is reproducible, and what remediation path it suggests.

RoboProof is not a robot policy tournament. In a policy tournament, miners submit robot policies and validators rank the best-performing policy. RoboProof inverts that design. Miners submit stress-test suites, adversarial simulation scenarios, failure hypotheses, and audit reports. Validators run those miner-generated tests against target policies and reference policy banks, rewarding audits that expose realistic, reproducible, safety-relevant policy failures.

The subnet produces one clear digital commodity:

Verified Robot Policy Reliability Audits

A Verified Robot Policy Reliability Audit is a replayable evidence package showing:

• where a robot policy fails;
• which failure predicate was triggered;
• whether the scenario is physically feasible;
• whether the failure reproduces under validator retests;
• whether the scenario breaks weak, medium, or strong policies;
• what retraining, recovery logic, or hardware validation step is recommended.

RoboProof starts with tabletop manipulation in Isaac Lab or MuJoCo. Phase 1 focuses on pick-and-place, stacking, insertion, and object repositioning tasks using open-source robot policies, public checkpoints, and validator-maintained reference policies. The goal is not to certify real-world safety from simulation alone. The narrower claim is that adversarial simulation audits can expose unsafe, unstable, brittle, or under-tested policies before expensive hardware trials.

This structure is especially well suited to Bittensor because robot failure modes are an open set, not a closed benchmark. A centralized red-team tool can improve a fixed test suite, but it is structurally difficult for one team to discover every grasp failure, recovery loop, contact instability, perception brittleness, or simulator-transfer edge case. RoboProof turns that open-ended search into a standing market: different miners specialize in different failure families, while emissions continually reward the miners that discover reproducible failures others missed.

2. Incentive & Mechanism Design

The incentive mechanism of RoboProof is designed to reward useful red-team intelligence rather than raw simulation volume. A miner is rewarded when their scenario suite finds feasible, reproducible, specific, and actionable robot policy failures. A miner is not rewarded for generating impossible tasks, simulator exploits, random parameter spam, or narrative-only audit reports.

Emission and Reward Logic: A Top-Weighted Audit Market

RoboProof receives emissions through Bittensor's subnet emission mechanism. Validators convert each miner's audit score into a weight vector and submit it through set_weights(), which is aggregated through Yuma Consensus.