提出新算法缓解AI辅助导致的人类技能退化问题
Proximal State Nudging: Reducing Skill Atrophy from AI Assistance
- 通过靠近最易学习状态来引导用户，兼顾学习与任务表现
- 人类实验中，技能提升最高达传统方法的7倍，事故减少50%
- 适合需要人机协作且注重长期能力培养的系统设计
技能退化——在半自动驾驶系统中，因依赖AI辅助导致操作者能力逐渐下降——带来了安全风险，因为操作者难以区分自身输入与自动修正。本文提出近似状态引导（Proximal State Nudging, PSN），一种共享自主算法，通过将用户引导至预估最具学习价值的状态，联合优化技能发展与任务性能。首先，在经典LunarLander仿真环境中，模拟学生实验显示PSN在提升无辅助奖励表现的同时，优于现有共享自主基线。随后，据我们所知，首次开展包含学习兼容性共享自主规划器的人体实验：在CARLA模拟器中的高性能竞速与平行泊车任务中（n=60），PSN相较标准混合共享自主方式，使无辅助技能提升最高达7倍，且碰撞次数比纯自主练习减少50%。
原文摘要 · Abstract (English)
Skill atrophy, the gradual decline of human capability under AI assistance, poses a safety risk in shared-control of semi-autonomous systems, where operators may be unable to distinguish their own inputs from autonomous corrections. We propose Proximal State Nudging (PSN), a shared autonomy algorithm that jointly optimizes for skill development and task performance by nudging users toward states estimated to be most learnable. We first show that PSN outperforms existing shared autonomy baselines in balancing student improvement in unassisted reward with overall shared performance, using simulated students in the classic LunarLander environment. We then present, to the best of our knowledge, the first human subject studies of a planner incorporating learning-compatible shared autonomy: across two driving tasks in the CARLA simulator (High Performance Racing and Parallel Parking, n = 60), PSN produces up to 7x larger gains in unassisted skill than standard blended shared autonomy, while incurring 50% fewer collisions than unassisted self-practice.
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