6,672 research outputs found
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 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
Geometry-aware Manipulability Learning, Tracking and Transfer
Body posture influences human and robots performance in manipulation tasks,
as appropriate poses facilitate motion or force exertion along different axes.
In robotics, manipulability ellipsoids arise as a powerful descriptor to
analyze, control and design the robot dexterity as a function of the
articulatory joint configuration. This descriptor can be designed according to
different task requirements, such as tracking a desired position or apply a
specific force. In this context, this paper presents a novel
\emph{manipulability transfer} framework, a method that allows robots to learn
and reproduce manipulability ellipsoids from expert demonstrations. The
proposed learning scheme is built on a tensor-based formulation of a Gaussian
mixture model that takes into account that manipulability ellipsoids lie on the
manifold of symmetric positive definite matrices. Learning is coupled with a
geometry-aware tracking controller allowing robots to follow a desired profile
of manipulability ellipsoids. Extensive evaluations in simulation with
redundant manipulators, a robotic hand and humanoids agents, as well as an
experiment with two real dual-arm systems validate the feasibility of the
approach.Comment: Accepted for publication in the Intl. Journal of Robotics Research
(IJRR). Website: https://sites.google.com/view/manipulability. Code:
https://github.com/NoemieJaquier/Manipulability. 24 pages, 20 figures, 3
tables, 4 appendice
Learning Riemannian Stable Dynamical Systems via Diffeomorphisms
Dexterous and autonomous robots should be capable of executing elaborated
dynamical motions skillfully. Learning techniques may be leveraged to build
models of such dynamic skills. To accomplish this, the learning model needs to
encode a stable vector field that resembles the desired motion dynamics. 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. To
tackle this problem, we propose learning Riemannian stable dynamical systems
(RSDS) from demonstrations, allowing us to account for different geometric
constraints resulting from the dynamical system state representation. Our
approach provides Lyapunov-stability guarantees on Riemannian manifolds that
are enforced on the desired motion dynamics via diffeomorphisms built on neural
manifold ODEs. We show that our Riemannian approach makes it possible to learn
stable dynamical systems displaying complicated vector fields on both
illustrative examples and real-world manipulation tasks, where Euclidean
approximations fail.Comment: To appear at CoRL 202
Negotiating Large Obstacles with a Humanoid Robot via Multi-Contact Motion Planning
Incremental progress in humanoid robot locomotion over the years has achieved essential capabilities such as navigation over
at or uneven terrain, stepping over small obstacles and imbing stairls. However, the locomotion research has mostly been limited to using only bipedal gait and only foot contacts with the environment, using the upper body for balancing without considering additional external contacts. As a result, challenging locomotion tasks like climbing over large obstacles relative to the size of the robot have remained unsolved. In this paper, we address this class of open problems with an approach based on multi-contact motion planning, guided by physical human demonstrations. Our goal is to make humanoid locomotion
problem more tractable by taking advantage of objects in the surrounding environment instead of avoiding them. We propose a multi-contact motion planning algorithm for humanoid robot locomotion which exploits the multi-contacts at the upper and lower body limbs. We propose a contact stability measure, which simplies the contact search from demonstration and
contact transition motion generation for the multi-contact motion planning algorithm. The algorithm uses the whole-body motions generated via Quadratic Programming (QP) based solver methods. The multi-contact motion planning algorithm is applied for a challenging task of climbing over a relatively larger obstacle compared to the robot. We validate our
planning approach with simulations and experiments for climbing over a large wooden obstacle with COMAN, which is a complaint humanoid robot with 23 degrees of freedom (DOF). We also propose a generalization method, the \Policy-Contraction Learning Method" to extend the algorithm for generating new multi-contact plans for our multi-contact motion planner, that can adapt to changes in the environment. The method learns a general policy and the multi-contact behavior from the human demonstrations, for generating new multi-contact plans for the obstacle-negotiation
Deep Metric Imitation Learning for Stable Motion Primitives
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 motions, a robot should consistently
reach its goal, regardless of its initial conditions. To meet this requirement,
IL methods often employ specialized function approximators that guarantee this
property by construction. Although effective, these approaches come with a set
of limitations: 1) they are unable to fully exploit the capabilities of modern
Deep Neural Network (DNN) architectures, 2) some are restricted in the family
of motions they can model, resulting in suboptimal IL capabilities, and 3) they
require explicit extensions to account for the geometry of motions that
consider orientations. To address these challenges, we introduce a novel
stability loss function, drawing inspiration from the triplet loss used in the
deep metric learning literature. This loss does not constrain the DNN's
architecture and enables learning policies that yield accurate results.
Furthermore, it is easily adaptable to the geometry of the robot's state space.
We provide a proof of the stability properties induced by this loss and
empirically validate our method in various settings. These settings include
Euclidean and non-Euclidean state spaces, as well as first-order and
second-order motions, both in simulation and with real robots. More details
about the experimental results can be found at: https://youtu.be/ZWKLGntCI6w.Comment: 21 pages, 15 figures, 4 table
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