Generalized Nonlinear and Finsler Geometry for Robotics
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Abstract (arXiv)
Robotics research has found numerous important applications of Riemannian geometry. Despite that, the concept remain challenging to many roboticists because the background material is complex and strikingly foreign. Beyond em Riemannian geometry, there are many natural generalizations in the mathematical literature -- areas such as Finsler geometry and spray geometry -- but those generalizations are largely inaccessible, and as a result there remain few applications within robotics. This paper presents a re-derivation of spray and Finsler geometries we found critical for the development of our recent work on a powerful behavioral design tool we call geometric fabrics. These derivations build from basic tools in advanced calculus and the calculus of variations making them more accessible to a robotics audience than standard presentations. We focus on the pragmatic and calculable results, avoiding the use of tensor notation to appeal to a broader audience, emphasizing geometric path consistency over ideas around connections and curvature. We hope that these derivations will contribute to an increased understanding of generalized nonlinear, and even classical Riemannian, geometry within the robotics community and inspire future research into new applications.
Key facts. Cited by 10 papers in the map (data through 7 Oct 2026). First posted on arXiv 28 Oct 2020.
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Citing papers per year: 2021: 3; 2022: 2; 2023: 1; 2024: 2; 2025: 1; 2026: 1.
10 papers.
- 2021Symmetric Spaces for Graph Embeddings: A Finsler-Riemannian Approachcited by 23
- 2021Vector-valued Distance and Gyrocalculus on the Space of Symmetric Positive Definite Matricescited by 22
- 2021Composable Energy Policies for Reactive Motion Generation and Reinforcement Learningcited by 16
- 2024Finsler-Laplace-Beltrami Operators with Application to Shape Analysiscited by 10
- 2022Hierarchical Policy Blending as Inference for Reactive Robot Controlcited by 8
- 2025Finsler Multi-Dimensional Scaling: Manifold Learning for Asymmetric Dimensionality Reduction and Embeddingcited by 7
- 2022Identifying latent distances with Finslerian geometrycited by 2
- 2024Learning Dynamical Systems Encoding Non-Linearity within Space Curvaturecited by 2
- 2023A Probabilistic Relaxation of the Two-Stage Object Pose Estimation Paradigmcited by 0
- 2026Harnessing Data Asymmetry: Manifold Learning in the Finsler Worldcited by 0
- 2026Harnessing Data Asymmetry: Manifold Learning in the Finsler World12 Mar 2026
- 2025Finsler Multi-Dimensional Scaling: Manifold Learning for Asymmetric Dimensionality Reduction and Embedding23 Mar 2025
- 2024Finsler-Laplace-Beltrami Operators with Application to Shape Analysis5 Apr 2024
- 2024Learning Dynamical Systems Encoding Non-Linearity within Space Curvature18 Mar 2024
- 2023A Probabilistic Relaxation of the Two-Stage Object Pose Estimation Paradigm1 Jun 2023
- 2022Identifying latent distances with Finslerian geometry20 Dec 2022
- 2022Hierarchical Policy Blending as Inference for Reactive Robot Control14 Oct 2022
- 2021Vector-valued Distance and Gyrocalculus on the Space of Symmetric Positive Definite Matrices26 Oct 2021
- 2021Symmetric Spaces for Graph Embeddings: A Finsler-Riemannian Approach9 Jun 2021
- 2021Composable Energy Policies for Reactive Motion Generation and Reinforcement Learning11 May 2021