Papers

@conference{Vieira:2024aa,
title = {MORALS: Analysis of High-Dimensional Robot Controllers Via Topological Tools in a Latent Space},
author = {E Vieira and A Sivaramakrishnan and S Tangirala and E Granados and K Mischaikow and K Bekris},
url = {https://arxiv.org/abs/2310.03246},
year = {2024},
date = {2024-05-01},
booktitle = {IEEE International Conference on Robotics and Automation (ICRA)},
address = {Yokohama, Japan (Nominated for Best Paper Award in Automation)},
abstract = {Estimating the region of attraction (𝚁𝚘𝙰) for a robotic system's controller is essential for safe application and controller composition. Many existing methods require access to a closed-form expression that limit applicability to data-driven controllers. Methods that operate only over trajectory rollouts tend to be data-hungry. In prior work, we have demonstrated that topological tools based on Morse Graphs offer data-efficient 𝚁𝚘𝙰 estimation without needing an analytical model. They struggle, however, with high-dimensional systems as they operate over a discretization of the state space. This paper presents Morse Graph-aided discovery of Regions of Attraction in a learned Latent Space (𝙼𝙾𝚁𝙰𝙻𝚂). The approach combines autoencoding neural networks with Morse Graphs. 𝙼𝙾𝚁𝙰𝙻𝚂 shows promising predictive capabilities in estimating attractors and their 𝚁𝚘𝙰s for data-driven controllers operating over high-dimensional systems, including a 67-dim humanoid robot and a 96-dim 3-fingered manipulator. It first projects the dynamics of the controlled system into a learned latent space. Then, it constructs a reduced form of Morse Graphs representing the bistability of the underlying dynamics, i.e., detecting when the controller results in a desired versus an undesired behavior. The evaluation on high-dimensional robotic datasets indicates the data efficiency of the approach in 𝚁𝚘𝙰 estimation.},
keywords = {Dynamics, Planning, Verification},
pubstate = {published},
tppubtype = {conference}
}