Zobrazeno 1 - 10
of 223
pro vyhledávání: '"Joel W Burdick"'
Publikováno v:
PLoS Biology, Vol 5, Iss 11, p e301 (2007)
Active sensing organisms, such as bats, dolphins, and weakly electric fish, generate a 3-D space for active sensation by emitting self-generated energy into the environment. For a weakly electric fish, we demonstrate that the electrosensory space for
Externí odkaz:
https://doaj.org/article/72142e4fdf0f41f6bc335d30b2c4cfc5
Autor:
Thomas Touma, Ersin Daş, Joel W. Burdick, Evan Bock Clark, Ryan Mackey, Martin S. Feather, Lorraine M. Fesq, Ksenia O. Kolcio, Maurice Prather
Publikováno v:
2023 IEEE Aerospace Conference.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::f074ddd33ea0f3caa3d381dbd9191566
https://doi.org/10.1109/aero55745.2023.10115718
https://doi.org/10.1109/aero55745.2023.10115718
Publikováno v:
IEEE Robotics and Automation Letters. 6:6220-6227
Many operations in robot-assisted surgery (RAS) can be viewed in a hierarchical manner. Each surgical task is represented by a superstate, which can be decomposed into finer-grained states. The estimation of these discrete states at different levels
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::df0604b76432a19cccfb6347472720a4
https://doi.org/10.1109/lra.2021.3091728
https://doi.org/10.1109/lra.2021.3091728
Publikováno v:
2022 International Conference on Robotics and Automation (ICRA).
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::c14751fbb22df0e838960f5bbd7de12d
https://doi.org/10.1109/icra46639.2022.9811896
https://doi.org/10.1109/icra46639.2022.9811896
Autor:
Kun Li, Joel W. Burdick
Publikováno v:
The International Journal of Robotics Research. 39:568-585
This work develops a novel high-dimensional inverse reinforcement learning (IRL) algorithm for human motion analysis in medical, clinical, and robotics applications. The method is based on the assumption that a surgical robot operators’ skill or a
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::487885cee1b31b34f5affcce6f311fe2
https://doi.org/10.1177/0278364920903104
https://doi.org/10.1177/0278364920903104
This paper proposes a new structured method for a moving agent to predict the paths of dynamically moving obstacles and avoid them using a risk-aware model predictive control (MPC) scheme. Given noisy measurements of the a priori unknown obstacle tra
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::3a0b3feb6f702d5804c35ae29377f227
Publikováno v:
ACC
Control barrier functions (CBFs) are a powerful tool to guarantee safety of autonomous systems, yet they rely on the computation of control invariant sets, which is notoriously difficult. A backup strategy employs an implicit control invariant set co
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::ec1bc04019a6cbbc7fd8f75828d47dcd
https://resolver.caltech.edu/CaltechAUTHORS:20210113-163505361
https://resolver.caltech.edu/CaltechAUTHORS:20210113-163505361
Autor:
Joel W. Burdick, Erica A. Dale, Michael A. Thornton, V. Reggie Edgerton, Patricia E. Phelps, Hui Zhong, Luke Stuart Urban, Katie L Ingraham Dixie
Publikováno v:
J Neurophysiol
Having observed that electrical spinal cord stimulation and training enabled four patients with paraplegia with motor complete paralysis to regain voluntary leg movement, the underlying mechanisms involved in forming the newly established supraspinal
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::bace28ff6d6d2e587c781865de901cf7
https://resolver.caltech.edu/CaltechAUTHORS:20211008-173253902
https://resolver.caltech.edu/CaltechAUTHORS:20211008-173253902
Publikováno v:
AAAI
Reinforcement Learning (RL) algorithms have found limited success beyond simulated applications, and one main reason is the absence of safety guarantees during the learning process. Real world systems would realistically fail or break before an optim
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::c6bda6f5490cec6659ad6fc4b0efeff2
https://doi.org/10.1609/aaai.v33i01.33013387
https://doi.org/10.1609/aaai.v33i01.33013387
Publikováno v:
ICRA
When autonomous robots interact with humans, such as during autonomous driving, explicit safety guarantees are crucial in order to avoid potentially life-threatening accidents. Many data-driven methods have explored learning probabilistic bounds over
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::78bd2562eeda5fdb24866bd2516e5321
https://doi.org/10.1109/icra48506.2021.9561843
https://doi.org/10.1109/icra48506.2021.9561843