Composite Gaussian Processes Flows for Learning Discontinuous Multimodal Policies
Learning control policies for real-world robotic tasks often involve challenges such as multimodality, local discontinuities, and the need for computational efficiency. These challenges arise from the complexity of robotic environments, where multiple solutions may coexist. To address these issues, we propose Composite Gaussian Processes Flows (CGP-Flows), a novel semi-parametric model for robotic policy. CGP-Flows integrate Overlapping Mixtures of Gaussian Processes (OMGPs) with the Continuous Normalizing Flows (CNFs), enabling them to model complex policies addressing multimodality and local discontinuities. This hybrid approach retains the computational efficiency of OMGPs while incorporating the flexibility of CNFs. Experiments conducted in both simulated and real-world robotic tasks demonstrate that CGP-flows significantly improve performance in modeling control policies. In a simulation task, we confirmed that CGP-Flows had a higher success rate compared to the baseline method, and the success rate of GCP-Flow was significantly different from the success rate of other baselines in chi-square tests.
Code (0)
등록된 구현이 없습니다.
Tasks
Computational EfficiencyGaussian ProcessesMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
On the continuity of flows
Flow matching has emerged as a powerful framework for generative modeling through continuous normalizing flows. We investigate a potential topological constraint: when the prior distribution and target distribution have …
Deep Multi-fidelity Gaussian Processes
We develop a novel multi-fidelity framework that goes far beyond the classical AR(1) Co-kriging scheme of Kennedy and O'Hagan (2000). Our method can handle general discontinuous cross-correlations among systems with diff…
Gaussian ProcessesComposite likelihood estimation of stationary Gaussian processes with a view toward stochastic volatility
We develop a framework for composite likelihood inference of parametric continuous-time stationary Gaussian processes. We derive the asymptotic theory of the associated maximum composite likelihood estimator. We implemen…
Gaussian ProcessesComposite Gaussian Processes: Scalable Computation and Performance Analysis
Gaussian process (GP) models provide a powerful tool for prediction but are computationally prohibitive using large data sets. In such scenarios, one has to resort to approximate methods. We derive an approximation based…
Gaussian ProcessesFuzzyBIO: A Proposal for Fuzzy Representation of Discontinuous Entities
Discontinuous entities pose a challenge to named entity recognition (NER). These phenomena occur commonly in the biomedical domain. As a solution, expansions of the BIO representation scheme that can handle these entity …
named-entity-recognitionNamed Entity RecognitionNamed Entity Recognition (NER)NER+1