Bayesian Optimization Meets Riemannian Manifolds in Robot Learning

2019/10/11 by Noémie Jaquier, Jaquier, Noémie, Leonel Rozo +5 · 2 citations
Computer Science · #Advanced Multi-Objective Optimization Algorithms #Evolutionary Algorithms and Applications #FOS: Computer and information sciences #Gaussian Processes and Bayesian Inference #Machine Learning (cs.LG) #Robotics (cs.RO)

paper · pdf · doi:10.48550/arxiv.1910.04998

openalex publication_date 2019/10/11 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

Bayesian optimization (BO) recently became popular in robotics to optimize control parameters and parametric policies in direct reinforcement learning due to its data efficiency and gradient-free approach. However, its performance may be seriously compromised when the parameter space is high-dimensional. A way to tackle this problem is to introduce domain knowledge into the BO framework. We propose to exploit the geometry of non-Euclidean parameter spaces, which often arise in robotics (e.g. orientation, stiffness matrix). Our approach, built on Riemannian manifold theory, allows BO to properly measure similarities in the parameter space through geometry-aware kernel functions and to optimize the acquisition function on the manifold as an unconstrained problem. We test our approach in several benchmark artificial landscapes and using a 7-DOF simulated robot to learn orientation and impedance parameters for manipulation skills.

Citations

• Taking the Human Out of the Loop: A Review of Bayesian Optimization
• GPflowOpt: A Bayesian Optimization Library using TensorFlow
• Deep Reinforcement Learning: A Brief Survey

Cited by

• Orientation Learning and Adaptation towards Simultaneous Incorporation of Multiple Local Constraints
• Causal-PIK: Causality-based Physical Reasoning with a Physics-Informed Kernel

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