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.

翻译：先进的人工智能生成内容（AIGC）技术为远程操作注入了新的动力，进一步提升了其安全性与效率。为满足远程操作严格的低延迟要求，边缘AIGC网络应运而生。然而，AIGC服务质量固有的不确定性以及激励AIGC服务提供商（ASP）的需求，使得设计一个鲁棒的激励机制至关重要。由于不确定性和信息不对称的双重挑战，这一设计尤为困难，因为远程操作者对ASP的剩余资源容量了解有限。为此，我们提出了一种基于分布鲁棒优化（DRO）的契约理论，用于设计AIGC任务卸载的鲁棒奖励方案。值得注意的是，我们的工作通过整合DRO扩展了契约理论，解决了不确定性下契约设计的根本性挑战。本文采用契约理论对信息不对称进行建模，同时利用DRO捕捉AIGC服务质量的不确定性。鉴于原始的基于DRO的契约理论问题固有的复杂性，我们将其重构为一个等价的、可处理的双层优化问题。为了高效求解此问题，我们开发了一种基于块坐标下降（BCD）的算法来推导鲁棒奖励方案。在我们基于Unity的远程操作平台上的仿真结果表明，与最先进的方法（即基于深度强化学习（DRL）的契约理论）相比，所提方法在不同程度的AIGC服务质量变化下，能将远程操作者效用提升2.7%至10.74%，并将ASP效用提升60.02%。代码与数据已在 https://github.com/Zijun0819/DRO-Contract-Theory 公开。