Learning-based shared control for open world, dexterous robot teleoperation

Teleoperation allows people to extend their perception and interaction capabilities to remote locations in cases where distance or safety constraints prevent them from physically traveling. It is especially useful for operation in “open-world” environments, environments that cannot be accurately modeled ahead of time, where traditional automation techniques often prove unreliable. However, while humans are extremely adept at a wide variety of manipulation tasks, transferring these skills through a robot has remained an open challenge for over half a century. Given constraints on an operator’s total cognitive load, high degree-of-freedom systems face a tradeoff between flexibility and precision. Giving the operator direct control over every joint on the robot provides them with the maximum flexibility to complete many kinds of tasks, but the high cognitive load associated with such an interface makes it impossible to properly coordinate the joints for tasks requiring high precision. In contrast, shared control systems that let the operator control the robot at a higher level of abstraction reduce this cognitive load and can improve the operator’s precision, but simultaneously reduce the flexibility of the interface to accomplish tasks for which it was not explicitly designed. Towards the goal of creating truly telepresent systems, this thesis introduces several learning techniques to dynamically adapt robotic teleoperation interfaces to specific users and tasks, allowing operators to more naturally express their intent and enabling greater transfer of their manipulation skills. This approach has lead to improvements in mappings for retargeting operator inputs to robot actions, harnessing the operator’s existing manipulation skills to flexibly complete many kinds of tasks while maintaining a high degree of precision.

Name thesis:degree_name

Ph.D.

Level thesis:degree_level

Dissertation

Discipline thesis:degree_discipline

Computer Science

Grantor

University of Illinois Urbana-Champaign

Year dc:date

2025

Author dc:creator

• Naughton, Patrick

Contributors dc:contributor

• Hauser, Kris
• Bretl, Timothy
• Lazebnik, Svetlana
• Pinto, Lerrel
• Srinivasa, Siddhartha

• Teleoperation
• Shared Control
• Dexterous Manipulation
• Machine Learning

Statement dc:rights

• Copyright 2025 Patrick Naughton

Language dc:language

en

Handle dc:identifier

https://hdl.handle.net/2142/132495

OAI identifier oai:identifier

oai:www.ideals.illinois.edu:2142/132495