115 research outputs found
Learning Robot Motions with Stable Dynamical Systems under Diffeomorphic Transformations
Neumann K, Steil JJ. Learning Robot Motions with Stable Dynamical Systems under Diffeomorphic Transformations. Robotics and Autonomous Systems. 2015;70(C):1-15
An Energy-based Approach to Ensure the Stability of Learned Dynamical Systems
Non-linear dynamical systems represent a compact, flexible, and robust tool
for reactive motion generation. The effectiveness of dynamical systems relies
on their ability to accurately represent stable motions. Several approaches
have been proposed to learn stable and accurate motions from demonstration.
Some approaches work by separating accuracy and stability into two learning
problems, which increases the number of open parameters and the overall
training time. Alternative solutions exploit single-step learning but restrict
the applicability to one regression technique. This paper presents a
single-step approach to learn stable and accurate motions that work with any
regression technique. The approach makes energy considerations on the learned
dynamics to stabilize the system at run-time while introducing small deviations
from the demonstrated motion. Since the initial value of the energy injected
into the system affects the reproduction accuracy, it is estimated from
training data using an efficient procedure. Experiments on a real robot and a
comparison on a public benchmark shows the effectiveness of the proposed
approach.Comment: Accepted at the International Conference on Robotics and Automation
202
Learning Stable Robotic Skills on Riemannian Manifolds
In this paper, we propose an approach to learn stable dynamical systems
evolving on Riemannian manifolds. The approach leverages a data-efficient
procedure to learn a diffeomorphic transformation that maps simple stable
dynamical systems onto complex robotic skills. By exploiting mathematical tools
from differential geometry, the method ensures that the learned skills fulfill
the geometric constraints imposed by the underlying manifolds, such as unit
quaternion (UQ) for orientation and symmetric positive definite (SPD) matrices
for impedance, while preserving the convergence to a given target. The proposed
approach is firstly tested in simulation on a public benchmark, obtained by
projecting Cartesian data into UQ and SPD manifolds, and compared with existing
approaches. Apart from evaluating the approach on a public benchmark, several
experiments were performed on a real robot performing bottle stacking in
different conditions and a drilling task in cooperation with a human operator.
The evaluation shows promising results in terms of learning accuracy and task
adaptation capabilities.Comment: 16 pages, 10 figures, journa
Neural Contractive Dynamical Systems
Stability guarantees are crucial when ensuring a fully autonomous robot does
not take undesirable or potentially harmful actions. Unfortunately, global
stability guarantees are hard to provide in dynamical systems learned from
data, especially when the learned dynamics are governed by neural networks. We
propose a novel methodology to learn neural contractive dynamical systems,
where our neural architecture ensures contraction, and hence, global stability.
To efficiently scale the method to high-dimensional dynamical systems, we
develop a variant of the variational autoencoder that learns dynamics in a
low-dimensional latent representation space while retaining contractive
stability after decoding. We further extend our approach to learning
contractive systems on the Lie group of rotations to account for full-pose
end-effector dynamic motions. The result is the first highly flexible learning
architecture that provides contractive stability guarantees with capability to
perform obstacle avoidance. Empirically, we demonstrate that our approach
encodes the desired dynamics more accurately than the current state-of-the-art,
which provides less strong stability guarantees
Learning a Stable Dynamic System with a Lyapunov Energy Function for Demonstratives Using Neural Networks
Autonomous Dynamic System (DS)-based algorithms hold a pivotal and
foundational role in the field of Learning from Demonstration (LfD).
Nevertheless, they confront the formidable challenge of striking a delicate
balance between achieving precision in learning and ensuring the overall
stability of the system. In response to this substantial challenge, this paper
introduces a novel DS algorithm rooted in neural network technology. This
algorithm not only possesses the capability to extract critical insights from
demonstration data but also demonstrates the capacity to learn a candidate
Lyapunov energy function that is consistent with the provided data. The model
presented in this paper employs a straightforward neural network architecture
that excels in fulfilling a dual objective: optimizing accuracy while
simultaneously preserving global stability. To comprehensively evaluate the
effectiveness of the proposed algorithm, rigorous assessments are conducted
using the LASA dataset, further reinforced by empirical validation through a
robotic experiment
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 Riemannian data in robotics include stiffness (symmetric and
positive definite matrix (SPD)) and orientation (unit quaternion (UQ))
trajectories. For Riemannian data, unlike Euclidean ones, different dimensions
are interconnected by geometric constraints which have to be properly
considered during the learning process. Using distance preserving mappings, our
approach transfers the data between their original manifold and the tangent
space, realizing the removing and re-fulfilling of the geometric constraints.
This allows to extend existing frameworks to learn stable skills from
Riemannian data while guaranteeing the stability of the learning results. The
ability of RiemannianFlow to learn various data patterns and the stability of
the learned models are experimentally shown on a dataset of manifold motions.
Further, we analyze from different perspectives the robustness of the model
with different hyperparameter combinations. It turns out that the model's
stability is not affected by different hyperparameters, a proper combination of
the hyperparameters leads to a significant improvement (up to 27.6%) of the
model accuracy. Last, we show the effectiveness of RiemannianFlow in a real
peg-in-hole (PiH) task where we need to generate stable and consistent position
and orientation trajectories for the robot starting from different initial
poses
Learning to represent surroundings, anticipate motion and take informed actions in unstructured environments
Contemporary robots have become exceptionally skilled at achieving specific tasks in structured environments. However, they often fail when faced with the limitless permutations of real-world unstructured environments. This motivates robotics methods which learn from experience, rather than follow a pre-defined set of rules. In this thesis, we present a range of learning-based methods aimed at enabling robots, operating in dynamic and unstructured environments, to better understand their surroundings, anticipate the actions of others, and take informed actions accordingly
- …