Zobrazeno 1 - 6
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pro vyhledávání: '"Saidi, Tristan"'
We introduce ORC-ManL, a new algorithm to prune spurious edges from nearest neighbor graphs using a criterion based on Ollivier-Ricci curvature and estimated metric distortion. Our motivation comes from manifold learning: we show that when the data g
Externí odkaz:
http://arxiv.org/abs/2410.01149
Autor:
Guo, Gabe, Saidi, Tristan, Terban, Maxwell, Valsecchi, Michele, Billinge, Simon JL, Lipson, Hod
A major challenge in materials science is the determination of the structure of nanometer sized objects. Here we present a novel approach that uses a generative machine learning model based on diffusion processes that is trained on 45,229 known struc
Externí odkaz:
http://arxiv.org/abs/2406.10796
Autor:
Khandate, Gagan, Saidi, Tristan L., Shang, Siqi, Chang, Eric T., Liu, Yang, Dennis, Seth, Adams, Johnson, Ciocarlie, Matei
We present a method for enabling Reinforcement Learning of motor control policies for complex skills such as dexterous manipulation. We posit that a key difficulty for training such policies is the difficulty of exploring the problem state space, as
Externí odkaz:
http://arxiv.org/abs/2401.15484
Autor:
Khandate, Gagan, Shang, Siqi, Chang, Eric T., Saidi, Tristan Luca, Liu, Yang, Dennis, Seth Matthew, Adams, Johnson, Ciocarlie, Matei
In this paper, we present a novel method for achieving dexterous manipulation of complex objects, while simultaneously securing the object without the use of passive support surfaces. We posit that a key difficulty for training such policies in a Rei
Externí odkaz:
http://arxiv.org/abs/2303.03486
Akademický článek
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Autor:
Khandate, Gagan, Saidi, Tristan L., Shang, Siqi, Chang, Eric T., Liu, Yang, Dennis, Seth, Adams, Johnson, Ciocarlie, Matei
Publikováno v:
Autonomous Robots; Oct2024, Vol. 48 Issue 7, p1-19, 19p
