Highly capable assistive robotic arms are well-poised to dramatically increase the independence of those with severe motor impairments, by reducing their dependence on caregivers to perform select activities of daily living. However, the more sophisticated a robotic arm, the more complicated its control. Intuitive operation remains a challenge that only increases with task complexity, and is exasperated by limited or low-dimensional control interfaces. Our solution is to introduce machine automation and intelligence. We propose a formalism for customizable shared control that enables users to customize the way they share control with intelligent assistive devices based on the user's abilities and preferences. In our formalism, the system arbitrates between user input and the autonomous policy prediction, based on the confidence it has in the policy's prediction and in the user's ability to perform the task. Moreover, the system is invisible and able to augment minimal teleoperation interfaces (e.g. Sip-N-PufO- This point is critical for users whose own control signals are limited, and are only able to operate minimal interfaces as a result. While dexterous manipulation can be difficult for a user to achieve using minimal control interfaces, full robot autonomy is often lacking in robustness or unsatisfactory for users who wish to retain some control authority. Assistive teleoperation offers a customized and robust alternative. We propose methods for customizing system components based on user data that can generalize to new situations. In particular, we aim to address the following research questions: QI How can the system adapt its arbitration function to a new user or task? Q2 How can a user achieve the optimal arbitration function for a given task? Q3 How can the robot learn good policies from user demonstration and interaction? Q4 Are there user-centric and/or task-centric measures of confidence? We test the proposed methods for customizing system components in user studies with high Spinal Cord Injury patients as well as uninjured subjects. The result will be a system that can learn from its user and improve over time. The larger goal is assistive device-here specifically, robotic arm- operation that is accessible to, and intuitive for, persons with extremely limited or no upper limb motor control.

Public Health Relevance

The ability to intuitively operate sophisticated assistive devices holds the potential to dramatically increase the independence of people with severe motor impairments. The solution we propose simplifies operation through the introduction of customizable machine automation. The proposed work will develop and asses, via user study, methods for customizing the form taken by the automation and how it shares control with the user.

National Institute of Health (NIH)
National Institute of Biomedical Imaging and Bioengineering (NIBIB)
Research Project (R01)
Project #
Application #
Study Section
Special Emphasis Panel (ZRG1)
Program Officer
Peng, Grace
Project Start
Project End
Budget Start
Budget End
Support Year
Fiscal Year
Total Cost
Indirect Cost
Northwestern University at Chicago
Engineering (All Types)
Biomed Engr/Col Engr/Engr Sta
United States
Zip Code
Gopinath, Deepak; Jain, Siddarth; Argall, Brenna D (2017) Human-in-the-Loop Optimization of Shared Autonomy in Assistive Robotics. IEEE Robot Autom Lett 2:247-254
Nikolaidis, Stefanos; Kuznetsov, Anton; Hsu, David et al. (2016) Formalizing Human-Robot Mutual Adaptation: A Bounded Memory Model. Proc ACM SIGCHI 2016:75-82
Klingensmith, Matthew; Sirinivasa, Siddartha S; Kaess, Michael (2016) Articulated Robot Motion for Simultaneous Localization and Mapping (ARM-SLAM). IEEE Robot Autom Lett 1:1156-1163
Herlant, Laura V; Holladay, Rachel M; Srinivasa, Siddhartha S (2016) Assistive Teleoperation of Robot Arms via Automatic Time-Optimal Mode Switching. Proc ACM SIGCHI 2016:35-42
Jain, Siddarth; Farshchiansadegh, Ali; Broad, Alexander et al. (2015) Assistive Robotic Manipulation through Shared Autonomy and a Body-Machine Interface. IEEE Int Conf Rehabil Robot 2015:526-531
Javdani, Shervin; Srinivasa, Siddhartha S; Bagnell, J Andrew (2015) Shared Autonomy via Hindsight Optimization. Robot Sci Syst 2015: