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Fusion dynamical systems with machine learning in imitation learning: A comprehensive overview
Imitation Learning (IL), also referred to as Learning from Demonstration (LfD), holds
significant promise for capturing expert motor skills through efficient imitation, facilitating …
significant promise for capturing expert motor skills through efficient imitation, facilitating …
Toolflownet: Robotic manipulation with tools via predicting tool flow from point clouds
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 …
geometry of a scene. Despite significant progress in classification and segmentation from …
Deep generative models in robotics: A survey on learning from multimodal demonstrations
Learning from Demonstrations, the field that proposes to learn robot behavior models from
data, is gaining popularity with the emergence of deep generative models. Although the …
data, is gaining popularity with the emergence of deep generative models. Although the …
[HTML][HTML] Learning stable robotic skills on Riemannian manifolds
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 …
Riemannian manifolds. Our approach leverages a data-efficient procedure to learn a …
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. RFMP leverages the efficient training and inference …
synthesizing robot visuomotor policies. RFMP leverages the efficient training and inference …
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. Often, this data comprises variables …
methods for processing, modeling, or synthesizing data. Often, this data comprises variables …
Learning deep robotic skills on Riemannian manifolds
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 …
learn complex and stable skills evolving on Riemannian manifolds. Examples of …
Fast and Robust Visuomotor Riemannian Flow Matching Policy
Diffusion-based visuomotor policies excel at learning complex robotic tasks by effectively
combining visual data with high-dimensional, multi-modal action distributions. However …
combining visual data with high-dimensional, multi-modal action distributions. However …
Task generalization with stability guarantees via elastic dynamical system motion policies
Dynamical System (DS) based Learning from Demonstration (LfD) allows learning of
reactive motion policies with stability and convergence guarantees from a few trajectories …
reactive motion policies with stability and convergence guarantees from a few trajectories …
PUMA: deep metric imitation learning for stable motion primitives
Imitation learning (IL) facilitates intuitive robotic programming. However, ensuring the
reliability of learned behaviors remains a challenge. In the context of reaching motions, a …
reliability of learned behaviors remains a challenge. In the context of reaching motions, a …