Distributionally Robust Contract Theory for Edge AIGC Services in Teleoperation

TL;DR

The paper tackles robust incentive design for AIGC offloading in edge-based teleoperation under information asymmetry and uncertain service quality. It integrates contract theory with Wasserstein-based distributional robust optimization to produce robust, differentiated reward schemes, reformulated into a tractable bi-level optimization and solved via a Block Coordinate Descent algorithm. Empirical results on a unity-based teleoperation platform show teleoperator utility improvements of about 2.7% to 10.74% under quality shifts and ASP utility gains of around 60.02% relative to a DRL-based contract baseline. This DRO-based framework offers a principled, robust mechanism for attracting teleoperators to use edge AIGC services while ensuring ASPs maintain positive utilities across varying conditions, with practical applicability to other uncertainty-heavy contract design problems.

Abstract

Advanced AI-Generated Content (AIGC) technologies have injected new impetus into teleoperation, further enhancing its security and efficiency. Edge AIGC networks have been introduced to meet the stringent low-latency requirements of teleoperation. However, the inherent uncertainty of AIGC service quality and the need to incentivize AIGC service providers (ASPs) make the design of a robust incentive mechanism essential. This design is particularly challenging due to both uncertainty and information asymmetry, as teleoperators have limited knowledge of the remaining resource capacities of ASPs. To this end, we propose a distributionally robust optimization (DRO)-based contract theory to design robust reward schemes for AIGC task offloading. Notably, our work extends the contract theory by integrating DRO, addressing the fundamental challenge of contract design under uncertainty. In this paper, contract theory is employed to model the information asymmetry, while DRO is utilized to capture the uncertainty in AIGC service quality. Given the inherent complexity of the original DRO-based contract theory problem, we reformulate it into an equivalent, tractable bi-level optimization problem. To efficiently solve this problem, we develop a Block Coordinate Descent (BCD)-based algorithm to derive robust reward schemes. Simulation results on our unity-based teleoperation platform demonstrate that the proposed method improves teleoperator utility by 2.7\% to 10.74\% under varying degrees of AIGC service quality shifts and increases ASP utility by 60.02\% compared to the SOTA method, i.e., Deep Reinforcement Learning (DRL)-based contract theory. The code and data are publicly available at https://github.com/Zijun0819/DRO-Contract-Theory.

Figures (17)

• Figure 1: The potential challenges of AIGC-empowered edge teleoperation, where the performance score is acquired via LPIPS zhang2018unreasonable and SSIM wang2004image of processed AIGC results.
• Figure 2: Framework of the distributional robust optimization-based contract theory.
• Figure 3: Teleoperator utility versus the magnitude of evaluation data distribution shifts under varying latency updating steps $\eta_L$.
• Figure 4: Teleoperator utility versus the magnitude of evaluation data distribution shifts when the number of training data $N$ is varied.
• Figure 5: Teleoperator utility versus the magnitude of evaluation data distribution shifts under varying confidence levels $\tau$.

Theorems & Definitions (12)

• Definition 1: Incentive Rationality
• Definition 2: Incentive Compatibility
• Proposition 1
• proof
• Lemma 1
• proof
• Lemma 2
• proof
• Lemma 3
• proof