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Bayesian Optimization Meets Riemannian Manifolds in Robot Learning

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Recent Advances in Robot Learning from Demonstration

Aude Billard, Athanasios Polydoros

In the context of robotics and automation, learning from demonstration (LfD) is the paradigm in which robots acquire new skills by learning to imitate an expert. The choice of LfD over other robot learning methods is compelling when ideal behavior can be n ...

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Escaping from saddle points on Riemannian manifolds

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We consider minimizing a nonconvex, smooth function f on a Riemannian manifold M. We show that a perturbed version of Riemannian gradient descent algorithm converges to a second-order stationary point (and hence is able to escape saddle points on the manif ...

2019

Improved Bounds for Discretization of Langevin Diffusions: Near-Optimal Rates without Convexity

Nicolas Henri Bernard Flammarion

We consider minimizing a nonconvex, smooth function

f

on a Riemannian manifold

\mathcal{M}

. We show that a perturbed version of Riemannian gradient descent algorithm converges to a second-order stationary point (and hence is able to escape saddle point ...

2019

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Yves Weinand

Using renewable resources: Sustainable timber construction aims to develop optimized design solutions through including the recent advancements in digital fabrication, robotic assembly, material science, and structural mechanics. Toward this end, IBOIS at ...

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Geometry-aware Robot Manipulability Transfer

Sylvain Calinon, Noémie Laure Gwendoline Jaquier

Humans naturally vary their body posture in order to quickly move or apply forces along specific directions. Such posture changes are strongly linked to the specific requirements of the task at hand, and therefore play a relevant role on task performance. ...

2018

Robot Learning with Task-Parameterized Generative Models

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Task-parameterized models provide a representation of movement/behavior that can adapt to a set of task parameters describing the current situation encountered by the robot, such as location of objects or landmarks in its workspace. This paper gives an ove ...

Springer2018

Learning Task-Space Synergies using Riemannian Geometry

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In the context of robotic control, synergies can form an elementary unit of behavior. By specifying task-dependent coordination behaviors at a low control level, one can achieve task-specific disturbance rejection. In this work we present an approach to le ...

IEEE2017

An Approach for Imitation Learning on Riemannian Manifolds

Sylvain Calinon

In imitation learning, multivariate Gaussians are widely used to encode robot behaviors. Such approaches do not provide the ability to properly represent end-effector orientation, as the distance metric in the space of orientations is not Euclidean. In thi ...

2017

Unsupervised Camera Localization in Crowded Spaces

Alexandre Massoud Alahi

Existing camera networks in public spaces such as train terminals or malls can help social robots to navigate crowded scenes. However, the localization of the cameras is required, i.e., the positions and poses of all cameras in a unique reference. In this ...

2017

Compliant snake robot locomotion on horizontal pipes

Auke Ijspeert, Kamilo Andres Melo Becerra, Mehmet Hasan Mutlu, Massimo Vespignani

In this paper we introduce a body-compliant Modular Snake Robot executing rolling gaits on different cylindrical geometries. In the state of the art it is considered that an active shape adaptation to the terrain while a gait is executed produces better pe ...

2015

Improved Bounds for Discretization of Langevin Diffusions: Near-Optimal Rates without Convexity

Nicolas Henri Bernard Flammarion

We consider minimizing a nonconvex, smooth function

f

on a Riemannian manifold

\mathcal{M}

. We show that a perturbed version of Riemannian gradient descent algorithm converges to a second-order stationary point (and hence is able to escape saddle point ...

2019