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Found 9 papers, 3 papers with code
Riemannian Flow Matching Policy for Robot Motion Learning
We introduce Riemannian Flow Matching Policies (RFMP), a novel model for learning and synthesizing robot visuomotor policies.
Transfer Learning in Robotics: An Upcoming Breakthrough? A Review of Promises and Challenges
Transfer learning is a conceptually-enticing paradigm in pursuit of truly intelligent embodied agents.
Unraveling the Single Tangent Space Fallacy: An Analysis and Clarification for Applying Riemannian Geometry in Robot Learning
In the realm of robotics, numerous downstream robotics tasks leverage machine learning methods for processing, modeling, or synthesizing data.
Bringing motion taxonomies to continuous domains via GPLVM on hyperbolic manifolds
This may be attributed to the lack of computational models that fill the gap between the discrete hierarchical structure of the taxonomy and the high-dimensional heterogeneous data associated to its categories.
Geometry-aware Bayesian Optimization in Robotics using Riemannian Matérn Kernels
Bayesian optimization is a data-efficient technique which can be used for control parameter tuning, parametric policy adaptation, and structure design in robotics.
High-Dimensional Bayesian Optimization via Nested Riemannian Manifolds
Despite the recent success of Bayesian optimization (BO) in a variety of applications where sample efficiency is imperative, its performance may be seriously compromised in settings characterized by high-dimensional parameter spaces.
Bayesian Optimization Meets Riemannian Manifolds in Robot Learning
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.
Learning from demonstration with model-based Gaussian process
In learning from demonstrations, it is often desirable to adapt the behavior of the robot as a function of the variability retrieved from human demonstrations and the (un)certainty encoded in different parts of the task.
Tensor-variate Mixture of Experts for Proportional Myographic Control of a Robotic Hand
The proposed formulation takes into account the underlying structure of the data and remains efficient when few training data are available.
