Qiao, G.
, Tao, Y.
, Ding, D.
and Erickson, Z.
(2025),
Incremental Learning for Robot Shared Autonomy, IEEE International Conference on Intelligent Robots and Systems, Hangzhou, CN, [online], https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=958705 (Accessed May 20, 2026)
Official websites use .gov
A .gov website belongs to an official government organization in the United States.
Secure .gov websites use HTTPS
A lock (
) or https:// means you’ve safely connected to the .gov website. Share sensitive information only on official, secure websites.
Incremental Learning for Robot Shared Autonomy
Published
Author(s)
, Yiran Tao, Dan Ding, Zackory Erickson
Abstract
Shared autonomy holds promise for improving the usability and accessibility of assistive robotic arms, but current methods often rely on costly expert demonstrations and remain static after pretraining, limiting their ability to handle real-world variations. Even with extensive training data, unforeseen challenges—especially those that fundamentally alter task dynamics, such as unexpected obstacles or spatial constraints—can cause assistive policies to break down, leading to ineffective or unreliable assistance. To address this, we propose ILSA, an Incrementally Learned Shared Autonomy framework that continuously refines its assistive policy through user interactions, adapting to real-world challenges beyond the scope of pre-collected data. At the core of ILSA is a structured fine-tuning mechanism that enables continual improvement with each interaction by effectively integrating limited new interaction data while helping to preserve prior knowledge, aiming for a balance between adaptation and generalization. A user study with 20 participants demonstrates ILSA's effectiveness, showing faster task completion and improved user experience compared to static alternatives. Code and videos are available at https://ilsa-robo.github.io/.Proceedings Title
IEEE International Conference on Intelligent Robots and Systems
Conference Dates
October 19-25, 2025
Conference Location
Hangzhou, CN
Conference Title
International Conference on Intelligent Robots and Systems (IROS 2025)
Pub Type
Conferences
Download Paper
Keywords
AI, Incremental learning, robotics
Citation
Issues
If you have any questions about this publication or are having problems accessing it, please contact [email protected].
Created October 19, 2025, Updated May 19, 2026