Master's Thesis

Generating Bouldering Problems

Final Thesis 4.28 MB

Author of thesis: Ing. Lukáš Foltyn

Acad. year: 2025/2026

Supervisor: Ing. Jan Brukner

Reviewer: Ing. Šimon Sedláček

Abstract:

This thesis investigates the generation of MoonBoard bouldering problems using a Decoder-Only Transformer model. The task is formulated as conditional autoregressive sequence generation, where routes are generated hold-by-hold based on the target grade and MoonBoard setup year. A dataset of MoonBoard problems is collected, preprocessed, and converted into token sequences suitable for machine learning. Different route representations and architectural choices are explored to design a suitable model for the task. The generated problems are evaluated using both objective metrics and human assessment on a real MoonBoard. The results show that the Transformer-based approach can generate plausible bouldering problems, but also reveal limitations in movement quality, climbability, and grade accuracy. Overall, the thesis demonstrates that a Decoder-Only Transformer is a viable approach for structured climbing route generation, while further work is needed to better capture the physical and subjective aspects of climbing.

Keywords:

MoonBoard, bouldering problem generation, neural networks, generative models, Decoder-Only Transformer, autoregressive generation, climbing route generation

Date of defence

25.06.2026

Result of the defence

Defended (thesis was successfully defended)

znamkaAznamka

Grading

A

Process of defence

Student nejprve prezentoval výsledky, kterých dosáhl v rámci své práce. Komise se poté seznámila s hodnocením vedoucího a posudkem oponenta práce. Student následně odpověděl na otázky přítomných. Komise se na základě posudku oponenta, hodnocení vedoucího, přednesené prezentace a odpovědí studenta na položené otázky rozhodla práci hodnotit stupněm A.

Topics for thesis defence

  1. Jaká je výhoda generování tras oproti výběru z datasetu?
  2. Jaký je přínos aplikace při generování?

Language of thesis

English

Faculty

Department

Study programme

Information Technology and Artificial Intelligence (MITAI)

Specialization

Machine Learning (NMAL)

Composition of Committee

prof. Dr. Ing. Jan Černocký (předseda)
prof. Ing. Hynek Heřmanský, Dr. Eng. (místopředseda)
prof. RNDr. Alexandr Meduna, CSc. (člen)
Ing. Michal Hradiš, Ph.D. (člen)
Ing. František Grézl, Ph.D. (člen)
Ing. Martin Fajčík, Ph.D. (člen)

Supervisor’s report
Ing. Jan Brukner

Lukáš šel do rizika s vlastním zadáním, pro které měl nadšení, a vyplatilo se. Na práci pracoval samostatně a o některých problémech, například se získáváním dat nebo při trénování modelu jsem se dozvěděl až zpětně, kdy byly úspěšně vyřešeny. Celkově jsem s postupem prací i s výsledky nadmíru spokojený, proto navrhuji hodnocení stupněm A. 

Evaluation criteria Verbal classification
Information about assignment

Práce se zabývala generováním a klasifikací boulderingových problémů. Jako netriviální hodnotím obě hlavní části práce, a to sběr dat bez jednoduchého API a také trénování transformeru na poměrně nové oblasti. Práci tak hodnotím jako spíše náročnou. Zadání bylo splněno i s rozšířením o webovou aplikaci. 

Activity during solution, consultations, communication

Práce byla odevzdána v předstihu a měl jsem možnost ji přečíst před odevzdáním. 

Publication activity, awards

Výsledná práce byla integrována do webové aplikace, kde si může uživatel vyzkoušet generování boulderingových problémů. Tato aplikace byla také k dispozici v lezeckém centru Hangar Brno. 

Work with literature

Student si sám aktivně vyhledával a studoval relevantní materiály, a to jak k samotné architektuře transformeru, tak k užší doméně klasifikace a generování MoonBoard problémů. Nastudované zdroje pak smysluplně využil při návrhu řešení i při interpretaci výsledků.

Activity during solution, consultations, communication

Konzultace probíhaly pravidelně, na konzultace chodil připravený a plnil případné dohodnuté práce na další konzultaci.

Points proposed by supervisor: 95

Grade proposed by supervisor: A

Reviewer’s report
Ing. Šimon Sedláček

This diploma thesis clearly demonstrates the student's excellent technical and writing skills. The results are not only interesting for their research value, pushing the SOTA in this domain forward, but also in terms of their practical value to intermediate climbers diving into the realm of standardized board training. Overall, it was a joy to read such an excellent thesis. I fully recommend the thesis for the awards.

Evaluation criteria Verbal classification Points Max. points
The extent to which the requirements of the assignment have been met

Evaluation level: assignment fulfilled, and the work contains significant extensions

The assignment was completed in full and at every point the student takes extra steps to carefully select and validate each part of the model design to ensure that the final model performs as well as possible. I especially commend the thorough architecture and hyperparameter search conducted in chapter 5 as well as the effort to collect human feedback.

Extent of the technical report

Evaluation level: is within the usual extent

The thesis length is within the standard range.

Presentation level of the technical report

The thesis is exceptionally well structured, has great flow, and contains all the necessary details. At no point during the review I found myself confused or lacking a piece of information.

100 100
Formal preparation of a technical report

The thesis is written in clean, perfect English. Typographically, I can in places see minor mishaps like trailing indefinite articles and single-digit numbers at the end of lines, and while I find the presence of these surprising given the overall quality of the thesis, they do not disrupt the reading and should not detract from the overall excellency of the work.

98 100
Work with literature

The thesis provides a comprehensive survey of prior work relevant to both the thesis topic itself as well as the methods used for the solution, and the student takes care to define the key points of differentiation of the presented work compared to prior work. The citations are in the vast majority relevant and accurate, however, in a few places the student uses survey papers to cite certain findings, which in my opinion should include a citation of the original work. Lastly, the bibliography contains a few arxiv/preprint entries which were published at a conference or in a journal and should be cited as such.

90 100
Realisation output

The resulting codebase is functional, approprietly-structured, usable and well-documented.

100 100
Usability of results

The work presents a novel method for generating Moonboard boulderproblems, which remains a largely unexplored problem in general. Apart from problem generation, the work also presents boulder grade classification performance that is superior to prior work. The work presents well-validated models that generate problems of quality that is generally more than acceptable by experienced climbers and from my own testing experience, I can attest that fully attest to this. The thesis warrants at least a core A publication which I strongly suggest the student should consider.

The difficulty of the assignment

Evaluation level: more difficult assignment

The thesis deals with a rather novel topic that remains largely unexplored, has limited data availability and presents significant evaluation challenges due to the inherent subjectivity of the sport of bouldering. 

Points proposed by reviewer: 98

Grade proposed by reviewer: A

Responsibility: Mgr. et Mgr. Hana Odstrčilová