2025/04/04 by Hu, Lifan
#FOS: Computer and information sciences #Machine Learning (cs.LG)
paper · doi:10.48550/arxiv.2504.03220
This work investigates the inverse problem of generator recovery in matrix Lie groups from discretized trajectories. Let G be a real matrix Lie group and \mathfrakg = Lie(G) its corresponding Lie algebra. A smooth trajectory γ(t) generated by a fixed Lie algebra element ξ∈ \mathfrakg follows the exponential flow γ(t) = g0 ⋅ exp(t ξ). The central task addressed in this work is the reconstruction of such a latent generator ξ from a discretized sequence of poses \g0, g1, …, gT\ ⊂ G, sampled at uniform time intervals. This problem is formulated as a data-driven regression from normalized sequences of discrete Lie algebra increments log(gt-1 gt+1) to the constant generator ξ∈ \mathfrakg. A feedforward neural network is trained to learn this mapping across several groups, including SE(2), SE(3), SO(3), and SL(2,ℝ). It demonstrates strong empirical accuracy under both clean and noisy conditions, which validates the viability of data-driven recovery of Lie group generators using shallow neural architectures. This is Lie-RL GitHub Repo https://github.com/Anormalm/LieRL-on-Trajectories. Feel free to make suggestions and collaborations!