Proximal State Nudging: Reducing Skill Atrophy from AI Assistance
Megha Srivastava, Jonathan Ouyang, Eric Zhou, Andrew Silva, Emily Sumner, Dorsa Sadigh, Yuchen Cui, Deepak Gopinath, Guy Rosman

TL;DR
Proximal State Nudging (PSN) is a novel shared autonomy algorithm that enhances skill retention and safety by guiding users toward learnable states, outperforming existing methods in simulations and human studies.
Contribution
The paper introduces PSN, the first shared autonomy planner designed to improve skill development and safety, validated through simulations and human experiments.
Findings
PSN outperforms existing shared autonomy baselines in the LunarLander environment.
In human driving tasks, PSN yields up to 7x larger skill gains than standard methods.
PSN reduces collisions by 50% compared to unassisted self-practice.
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),…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
