Distributionally Robust Contract Theory for Edge AIGC Services in Teleoperation

2025-05-10

AI Summary

In edge-AIGC-enabled teleoperation scenarios, service quality uncertainty and information asymmetry between operators and service providers undermine the robustness of incentive mechanisms. Method: This work pioneers the integration of distributionally robust optimization (DRO) with principal-agent contract theory to construct a bilevel robust contract model resilient to unknown distributional shifts—eliminating reliance on prior distribution assumptions. An efficient block-coordinate descent (BCD)-based algorithm is proposed for tractable optimization. Contribution/Results: Evaluated on a Unity-based teleoperation platform, the framework increases operator utility by 2.7%–10.74% and improves provider utility by 60.02% over the state-of-the-art deep reinforcement learning (DRL)-based contract design. To our knowledge, this is the first distributionally robust incentive design framework tailored for low-latency, high-uncertainty edge-AIGC services.

Technology Category

• Multiagent Systems: Mechanism Design
• Intelligent Robots: Learning & Optimization for ROB
• Search and Optimization: Distributed Search

Application Category

• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Search and Retrieval-Augmented AI: Agentic search

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.

Problem

• Design robust incentive for AIGC service providers
• Address uncertainty in AIGC service quality
• Overcome information asymmetry in teleoperation

Innovation

• DRO-based contract theory for robust incentives
• Bi-level optimization reformulation for tractability
• BCD algorithm for efficient solution derivation