Maegan Tucker
Maegan Tucker
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Zitiert von
Zitiert von
Preference-based learning for exoskeleton gait optimization
M Tucker, E Novoseller, C Kann, Y Sui, Y Yue, JW Burdick, AD Ames
2020 IEEE international conference on robotics and automation (ICRA), 2351-2357, 2020
Towards variable assistance for lower body exoskeletons
T Gurriet, M Tucker, A Duburcq, G Boeris, AD Ames
IEEE Robotics and Automation Letters 5 (1), 266-273, 2019
Human preference-based learning for high-dimensional optimization of exoskeleton walking gaits
M Tucker, M Cheng, E Novoseller, R Cheng, Y Yue, JW Burdick, AD Ames
2020 IEEE/RSJ International Conference on Intelligent Robots and Systems …, 2020
Roial: Region of interest active learning for characterizing exoskeleton gait preference landscapes
K Li, M Tucker, E Bıyık, E Novoseller, JW Burdick, Y Sui, D Sadigh, Y Yue, ...
2021 IEEE International Conference on Robotics and Automation (ICRA), 3212-3218, 2021
Preference-based learning for user-guided hzd gait generation on bipedal walking robots
M Tucker, N Csomay-Shanklin, WL Ma, AD Ames
2021 IEEE International Conference on Robotics and Automation (ICRA), 2804-2810, 2021
Safety-aware preference-based learning for safety-critical control
R Cosner, M Tucker, A Taylor, K Li, T Molnar, W Ubelacker, A Alan, ...
Learning for Dynamics and Control Conference, 1020-1033, 2022
Evaluation of safety and performance of the self balancing walking system Atalante in patients with complete motor spinal cord injury
J Kerdraon, JG Previnaire, M Tucker, P Coignard, W Allegre, E Knappen, ...
Spinal Cord Series and Cases 7 (1), 1-8, 2021
Natural multicontact walking for robotic assistive devices via musculoskeletal models and hybrid zero dynamics
K Li, M Tucker, R Gehlhar, Y Yue, AD Ames
IEEE Robotics and Automation Letters 7 (2), 4283-4290, 2022
Learning controller gains on bipedal walking robots via user preferences
N Csomay-Shanklin, M Tucker, M Dai, J Reher, AD Ames
2022 International Conference on Robotics and Automation (ICRA), 10405-10411, 2022
A review of current state-of-the-art control methods for lower-limb powered prostheses
R Gehlhar, M Tucker, AJ Young, AD Ames
Annual Reviews in Control, 2023
Robust bipedal locomotion: Leveraging saltation matrices for gait optimization
M Tucker, N Csomay-Shanklin, AD Ames
2023 IEEE International Conference on Robotics and Automation (ICRA), 12218 …, 2023
POLAR: Preference Optimization and Learning Algorithms for Robotics
M Tucker, K Li, Y Yue, AD Ames
arXiv preprint arXiv:2208.04404, 2022
An input-to-state stability perspective on robust locomotion
M Tucker, AD Ames
IEEE Control Systems Letters, 2023
Stabilization of Exoskeletons through Active Ankle Compensation
T Gurriet, M Tucker, C Kann, G Boeris, AD Ames
arXiv preprint arXiv:1909.11848, 2019
Humanoid Robot Co-Design: Coupling Hardware Design with Gait Generation via Hybrid Zero Dynamics
AB Ghansah, J Kim, M Tucker, AD Ames
arXiv preprint arXiv:2308.10962, 2023
Leveraging User Preference in the Design and Evaluation of Lower-Limb Exoskeletons and Prostheses
KA Ingraham, M Tucker, AD Ames, EJ Rouse, MK Shepherd
Current Opinion in Biomedical Engineering, 100487, 2023
Input-to-State Stability in Probability
P Culbertson, RK Cosner, M Tucker, AD Ames
arXiv preprint arXiv:2304.14578, 2023
Enabling Robust and User-Customized Bipedal Locomotion on Lower-Body Assistive Devices via Hybrid System Theory and Preference-Based Learning
M Tucker
California Institute of Technology, 2023
Real-time feedback module for assistive gait training, improved proprioception, and fall prevention
M Tucker, AD Ames
US Patent App. 16/938,654, 2021
Preference-Based Bayesian Optimization in High Dimensions with Human Feedback
M Cheng, E Novoseller, M Tucker, R Cheng, J Burdick, Y Yue
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