{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/tt-morals-analysis-of-high-dimensional-robot","title":"${\\tt MORALS}$: Analysis of High-Dimensional Robot Controllers via Topological Tools in a Latent Space","arxiv_id":"2310.03246","date":"2023-10-05","proceeding":null,"authors":["Ewerton R. Vieira","Aravind Sivaramakrishnan","Sumanth Tangirala","Edgar Granados","Konstantin Mischaikow","Kostas E. Bekris"],"abstract":"Estimating the region of attraction (${\\tt RoA}$) for a robot controller is essential for safe application and controller composition. Many existing methods require 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 ${\\it Morse Graphs}$ (directed acyclic graphs that combinatorially represent the underlying nonlinear dynamics) offer data-efficient ${\\tt RoA}$ estimation without needing an analytical model. They struggle, however, with high-dimensional systems as they operate over a state-space discretization. This paper presents ${\\it Mo}$rse Graph-aided discovery of ${\\it R}$egions of ${\\it A}$ttraction in a learned ${\\it L}$atent ${\\it S}$pace (${\\tt MORALS}$). The approach combines auto-encoding neural networks with Morse Graphs. ${\\tt MORALS}$ shows promising predictive capabilities in estimating attractors and their ${\\tt RoA}$s 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 data efficiency in ${\\tt RoA}$ estimation.","url_abs":"https://arxiv.org/abs/2310.03246v2","url_pdf":"https://arxiv.org/pdf/2310.03246v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"links_only","authors_date_abstract":"arXiv metadata, CC0 1.0 (https://info.arxiv.org/help/license), from the Kaggle arXiv metadata snapshot of 2026-09-12"},"code_links":[{"paper_slug":"tt-morals-analysis-of-high-dimensional-robot","repo_url":"https://github.com/ewerton-vieira/morals","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}