Proximal State Nudging: Reducing Skill Atrophy from AI Assistance
Researchers propose Proximal State Nudging (PSN), a shared autonomy algorithm that reduces skill atrophy by nudging users toward learnable states. In simulated and human studies, PSN improved unassisted skill by up to 7x with 50% fewer collisions compared to baselines.
[2605.20355] Proximal State Nudging: Reducing Skill Atrophy from AI Assistance
[Submitted on 19 May 2026]
Title:Proximal State Nudging: Reducing Skill Atrophy from AI Assistance
View a PDF of the paper titled Proximal State Nudging: Reducing Skill Atrophy from AI Assistance, by Megha Srivastava and 8 other authors
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Abstract: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.
Comments: 9 pages
Subjects:
Robotics (cs.RO); Human-Computer Interaction (cs.HC); Machine Learning (cs.LG)
Cite as: arXiv:2605.20355 [cs.RO]
(or arXiv:2605.20355v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2605.20355
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Megha Srivastava [view email] [v1] Tue, 19 May 2026 18:10:46 UTC (13,494 KB)
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