Point clouds are a widely available and canonical data modality which convey the 3D
geometry of a scene. Despite significant progress in classification and segmentation from …

Imitation Learning (IL), also referred to as Learning from Demonstration (LfD), holds
significant promise for capturing expert motor skills through efficient imitation, facilitating …

A learning-based modular motion planning pipeline is presented that is compliant, safe, and
reactive to perturbations at task execution. A nominal motion plan, defined as a nonlinear …

In this paper, we propose an approach to learn stable dynamical systems that evolve on
Riemannian manifolds. Our approach leverages a data-efficient procedure to learn a …

In this paper, we propose RiemannianFlow, a deep generative model that allows robots to
learn complex and stable skills evolving on Riemannian manifolds. Examples of …

Imitation Learning (IL) is a powerful technique for intuitive robotic programming. However,
ensuring the reliability of learned behaviors remains a challenge. In the context of reaching …

In the realm of robotics, numerous downstream robotics tasks leverage machine learning
methods for processing, modeling, or synthesizing data. Often, this data comprises variables …

Dynamical System (DS) based Learning from Demonstration (LfD) allows learning of
reactive motion policies with stability and convergence guarantees from a few trajectories …

We introduce Riemannian Flow Matching Policies (RFMP), a novel model for learning and
synthesizing robot visuomotor policies. RFMP leverages the efficient training and inference …

We propose the idea of using Kuramoto models (including their higher-dimensional
generalizations) for machine learning over non-Euclidean data sets. These models are …