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Found 16 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.
Neural Contractive Dynamical Systems
Stability guarantees are crucial when ensuring a fully autonomous robot does not take undesirable or potentially harmful actions.
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.
Learning Riemannian Stable Dynamical Systems via Diffeomorphisms
This is challenging as the robot state does not evolve on a Euclidean space, and therefore the stability guarantees and vector field encoding need to account for the geometry arising from, for example, the orientation representation.
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.
Reactive Motion Generation on Learned Riemannian Manifolds
We argue that Riemannian manifolds may be learned via human demonstrations in which geodesics are natural motion skills.
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.
Orientation Probabilistic Movement Primitives on Riemannian Manifolds
Learning complex robot motions necessarily demands to have models that are able to encode and retrieve full-pose trajectories when tasks are defined in operational spaces.
Learning Riemannian Manifolds for Geodesic Motion Skills
For robots to work alongside humans and perform in unstructured environments, they must learn new motion skills and adapt them to unseen situations on the fly.
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.
Learning and Sequencing of Object-Centric Manipulation Skills for Industrial Tasks
Furthermore, to accomplish complex manipulation tasks, robots should be able to sequence several skills and adapt them to changing situations.
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.
Interactive Trajectory Adaptation through Force-guided Bayesian Optimization
Flexible manufacturing processes demand robots to easily adapt to changes in the environment and interact with humans.
Hierarchical Reinforcement Learning for Concurrent Discovery of Compound and Composable Policies
Moreover, such composition of individual policies is usually performed sequentially, which is not suitable for tasks that require to perform the subtasks concurrently.
Hierarchical Reinforcement Learning
reinforcement-learning
+1
Uncertainty-Aware Imitation Learning using Kernelized Movement Primitives
This rich set of information is used in combination with optimal controller fusion to learn actions from data, with two main advantages: i) robots become safe when uncertain about their actions and ii) they are able to leverage partial demonstrations, given as elementary sub-tasks, to optimally perform a higher level, more complex task.
Probabilistic Learning of Torque Controllers from Kinematic and Force Constraints
When learning skills from demonstrations, one is often required to think in advance about the appropriate task representation (usually in either operational or configuration space).
