Zobrazeno 1 - 7
of 7
pro vyhledávání: '"Arun Venkatraman"'
Autor:
Jennifer L. Collinger, John E. Downey, Katharina Muelling, J. Andrew Bagnell, Andrew B. Schwartz, Arun Venkatraman, Martial Hebert, Shervin Javdani, Jean-Sebastien Valois, Jeffrey M Weiss
Publikováno v:
Autonomous Robots. 41:1401-1422
Robot teleoperation systems face a common set of challenges including latency, low-dimensional user commands, and asymmetric control inputs. User control with Brain---Computer Interfaces (BCIs) exacerbates these problems through especially noisy and
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::3311bce6dad4d7a61801eb94f9e1ea2c
https://doi.org/10.1007/s10514-017-9622-4
https://doi.org/10.1007/s10514-017-9622-4
Autor:
Arun Venkatraman, Jennifer L. Collinger, Jeffrey M Weiss, John E. Downey, Jean-Sebastien Valois, Katharina Mülling, Andrew B. Schwartz, Shervin Javdani, Martial Hebert, J. Andrew Bagnell
Publikováno v:
Robotics: Science and Systems
Robot teleoperation systems face a common set of challenges including latency, low-dimensional user commands, and asymmetric control inputs. User control with Brain- Computer Interfaces (BCIs) exacerbates these problems through especially noisy and e
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::8c0053ad048f9448b3a4006f9c5077ac
Autor:
Lerrel Pinto, Arun Venkatraman, Roberto Capobianco, Daniele Nardi, J. Andrew Bagnell, Martial Hebert
Publikováno v:
Springer Proceedings in Advanced Robotics ISBN: 9783319501147
ISER
ISER
Model-based reinforcement learning (MBRL) plays an important role in developing control strategies for robotic systems. However, when dealing with complex platforms, it is difficult to model systems dynamics with analytic models. While data-driven to
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::b290f9397a38da3c0a76095e2e65ce65
http://hdl.handle.net/11573/928064
http://hdl.handle.net/11573/928064
Publikováno v:
Proceedings of the AAAI Conference on Artificial Intelligence. 30
Instrumental variable regression (IVR) is a statistical technique utilized to recover unbiased estimators when there are errors in the independent variables. Estimator bias in learned time series models can yield poor performance in applications such
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::2fb22de148b8f96afa11bba3d18c2e3d
https://doi.org/10.1609/aaai.v30i1.10215
https://doi.org/10.1609/aaai.v30i1.10215
Autor:
John E. Downey, Jean-Sebastien Valois, Jennifer L. Collinger, Jeffrey M Weiss, Arun Venkatraman, Andrew B. Schwartz, Martial Hebert, J. Andrew Bagnell, Katharina Muelling
Publikováno v:
Journal of NeuroEngineering and Rehabilitation
Background Recent studies have shown that brain-machine interfaces (BMIs) offer great potential for restoring upper limb function. However, grasping objects is a complicated task and the signals extracted from the brain may not always be capable of d
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::f106f511f5929d45a7db50be1eb84d4f
Publikováno v:
Proceedings of the AAAI Conference on Artificial Intelligence. 29
Most typical statistical and machine learning approaches to time series modeling optimize a single-step prediction error. In multiple-step simulation, the learned model is iteratively applied, feeding through the previous output as its new input. Any
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::7dfa43215e1b361ae5e56fb0b5a2299b
https://doi.org/10.1609/aaai.v29i1.9590
https://doi.org/10.1609/aaai.v29i1.9590
Publikováno v:
Robotics: Science and Systems
Autonomous manipulation in unstructured environments will enable a large variety of exciting and important applications. Despite its promise, autonomous manipulation remains largely unsolved. Even the most rudimentary manipulation task--such as remov
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::c001bc7458194fb2bae9891d78e683b7
https://doi.org/10.15607/rss.2013.ix.039
https://doi.org/10.15607/rss.2013.ix.039