AI 中文总结
本文提出一种合作博弈论框架，用于解决人机共享自主中的权限切换问题，推导了线性二次系统下最优切换策略的闭式递推式，验证了其在不同系统场景下的有效性，揭示了人类适应性与自主效率的权衡。
AI 中文摘要
共享自主需要在人与自主智能体之间分配和转移控制权的原则性机制。现有方法常依赖控制输入融合或启发式切换规则，缺乏理论保证且未考虑权限转移的动态特性。本文开发了一种用于共享自主中权限切换的合作博弈论框架，将控制切换问题表述为嵌入权限转移到系统动态的利益一致动态博弈，从而得到最优切换策略而非临时规则。在随机人工覆盖（override）场景下，考虑人类保留覆盖能力的非对称权限，本文证明了纯策略下团队最优策略的存在性与特性。针对线性二次系统，本文推导了最优切换策略与价值函数的闭式递推式，实现了不依赖连续状态的高效计算。本文在标量与多维线性系统上验证了该框架，展示了最优切换如何适应不同系统动态、成本结构与覆盖概率。结果揭示了人类适应性与自主效率之间的基本权衡，阐明了将共享自主建立在合作博弈论基础上的实际益处。
英文摘要
Shared autonomy requires principled mechanisms for allocating and transferring control between a human and an autonomous agent. Existing approaches often rely on blending control inputs or heuristic switching rules, which lack theoretical guarantees and fail to account for the dynamics of authority transfer. This paper develops a cooperative game-theoretic framework for authority switching in shared autonomy. We formulate the control switching problem as an identical-interest dynamic game in which authority transitions are embedded into the system dynamics, yielding optimal switching policies rather than ad hoc rules. We establish the existence and characterization of team-optimal policies in pure strategies under stochastic human override, accounting for asymmetric authority where humans retain override capability. For linear-quadratic systems, we derive closed-form recursions for the optimal switching policies and value functions, enabling efficient computation independent of the continuous state. We validate the framework on scalar and multi-dimensional linear systems, demonstrating how optimal switching adapts to varying system dynamics, cost structures, and override probabilities. The results reveal fundamental trade-offs between human adaptability and autonomous efficiency, illustrating the practical benefits of grounding shared autonomy in cooperative game theory.
Comments8 pages, 7 figures, accepted at IEEE RO-MAN 2026
