Tootfinder

Learning to Move in Rhythm: Task-Conditioned Motion Policies with Orbital Stability Guarantees
Maximilian St\"olzle, T. Konstantin Rusch, Zach J. Patterson, Rodrigo P\'erez-Dattari, Francesco Stella, Josie Hughes, Cosimo Della Santina, Daniela Rus
https://arxiv.org/abs/2507.10602

Learning to Move in Rhythm: Task-Conditioned Motion Policies with Orbital Stability Guarantees
Learning from demonstration provides a sample-efficient approach to acquiring complex behaviors, enabling robots to move robustly, compliantly, and with fluidity. In this context, Dynamic Motion Primitives offer built - in stability and robustness to disturbances but often struggle to capture complex periodic behaviors. Moreover, they are limited in their ability to interpolate between different tasks. These shortcomings substantially narrow their applicability, excluding a wide class of practi…

This https://arxiv.org/abs/2503.15954 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_hept…

Holographic deformations of matrix models
We study maximal supergravity in two dimensions, obtained from reduction of IIA supergravity on an $S^8$ sphere. The theory captures the low-lying fluctuations around the non-conformal D0-brane near-horizon geometry, dual to operators in the BFSS matrix model. Upon exciting some of the supergravity scalars, we construct half-supersymmetric domain wall solutions preserving $SO(p)\times SO(9-p)$ subgroups of the original $SO(9)$ symmetry. We determine their uplift to ten dimensions and the corres…