Zobrazeno 1 - 9
of 9
pro vyhledávání: '"Priyam Parashar"'
Publikováno v:
Frontiers in Physics, Vol 10 (2022)
Robots tasked with object assembly by manipulation of parts require not only a high-level plan for order of placement of parts but also detailed low-level information on how to place and pick the part based on its state. This is a complex multi-level
Externí odkaz:
https://doaj.org/article/8ce94bebfbd6428aa64aef388de642d5
Publikováno v:
CASE
The manufacturing world is moving towards a setup with high mix and medium volume. This requires new production paradigms. The World Robotics Challenge (2018 & 2020) was designed to challenge teams to design systems that are easy to adapt to new task
Autor:
Ashok K. Goel, Priyam Parashar
Publikováno v:
Systems Engineering and Artificial Intelligence ISBN: 9783030772826
As robots become increasingly pervasive in human society, there is a need for developing theoretical frameworks for “human–machine shared contexts.” In this chapter, we develop a framework for endowing robots with a human-like capacity for meta
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::ed21fbf9c8d49b5ed7493589f5eb4f3a
https://doi.org/10.1007/978-3-030-77283-3_21
https://doi.org/10.1007/978-3-030-77283-3_21
Robots share a conundrum central to all intelligence. Like humans, robots not only must address novel situations but also must start from what they already know: how, then, can any robot deal with novelty? In this chapter, we examine two cognitive st
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::4ab2e9e080da0e17b01e94b3d7acccec
https://doi.org/10.1016/b978-0-12-820543-3.00002-x
https://doi.org/10.1016/b978-0-12-820543-3.00002-x
Autor:
Olivier Bartheye, Jonathan Barzilai, Tyler Bath, Genevieve Bell, Caitlin M. Bentley, Derek Brock, Daniel J. Brooks, Sophie Brusniak, Beth Cardier, Niccolo Casas, Laurent Chaudron, Shu-Heng Chen, Richard Ciavarra, Michael Crowell, Dalton J. Curtin, Katherine A. Daniell, Noel Derwort, Saikou Diallo, Zac Hatfield Dodds, Sheng Dong, Tesca Fitzgerald, Hesham Fouad, Boris Galitsky, Ashok K. Goel, H.T. Goranson, Ariel M. Greenberg, Brian Jalaian, Joseph Knierman, James T. Kuczynski, William F. Lawless, Kobi Leins, John Licato, James Llinas, Patrick Lundberg, Amy K. McLennan, D. Douglas Miller, Ranjeev Mittu, Ira S. Moskowitz, Ehsan Nabavi, Alex Nielsen, Walt Panfil, Priyam Parashar, Sooyoung Park, Ryan Quandt, Ali K. Raz, Joshua J. Rodriguez, Stephen Russell, Geoffrey W. Rutledge, Larry D. Sanford, Mitchell Schmidt, Michael Scott, John Shull, Donald A. Sofge, Aaron Steinfeld, Adit Suvarna, Lisa Troyer, Elizabeth T. Williams, Michael Wollowski, Elena A. Wood, Joseph C. Wood, Holly A. Yanco
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::8833a0335e41a4ff7d766166022fc105
https://doi.org/10.1016/b978-0-12-820543-3.09991-0
https://doi.org/10.1016/b978-0-12-820543-3.09991-0
Publikováno v:
IROS
Other repository
Other repository
© 2019 IEEE. As robots and other autonomous agents are increasingly incorporated into complex domains, characterizing interaction within heterogeneous teams that include both humans and machines becomes more necessary. Previous literature has addres
Publikováno v:
The Knowledge Engineering Review. 33
We consider task planning for long-living intelligent agents situated in dynamic environments. Specifically, we address the problem of incomplete knowledge of the world due to the addition of new objects with unknown action models. We propose a multi
Publikováno v:
Autonomous Agents and Multiagent Systems ISBN: 9783319716787
AAMAS Workshops (Visionary Papers)
AAMAS Workshops (Visionary Papers)
We present a pilot study focused on creating flexible Hierarchical Task Networks that can leverage Reinforcement Learning to repair and adapt incomplete plans in the simulated rich domain of Minecraft. This paper presents an early evaluation of our a
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::929fc75bec7886d86a41a21771799fa8
https://doi.org/10.1007/978-3-319-71679-4_6
https://doi.org/10.1007/978-3-319-71679-4_6
Publikováno v:
ICMLA
We present a method to learn context-dependent outcomes of behaviors in unstructured indoor environments. The idea is that certain features in the environment may be predictive of differences in outcomes, such as how long a mobile robot takes to trav