Learning by Imitation with the STIFF-FLOP Surgical Robot: A Biomimetic Approach Inspired by Octopus Movements

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In various robotics applications, the selection of function approximation methods greatly influences the feasibility and computational efficiency of algorithms. Tensor Networks (TNs), also referred to as tensor decomposition techniques, present a versatile ...

Dynamical system (DS) based motion planning offers collision-free motion, with closed-loop reactivity thanks to their analytical expression. It ensures that obstacles are not penetrated by reshaping a nominal DS through matrix modulation, which is construc ...

In this thesis, we concentrate on advancing high-level behavioral control policies for robotic systems within the framework of Dynamical Systems (DS). Throughout the course of this research, a unifying thread weaving through diverse fields emerges, and tha ...

Robots outside of the fenced factories have to deal with continuously changing environment, this requires fast and flexible modes of control. Planning methods or complex learning models can find optimal paths in complex surroundings, but they are computati ...

In recent years, soft robotics has surged in applications like wearables, drones, smart fabrics, and medical instruments. Due to their compliance, these devices excel in tasks demanding dexterity and adaptability, such as manipulation, locomotion, crash re ...

We study the problem of performance optimization of closed -loop control systems with unmodeled dynamics. Bayesian optimization (BO) has been demonstrated to be effective for improving closed -loop performance by automatically tuning controller gains or re ...

While momentum-based accelerated variants of stochastic gradient descent (SGD) are widely used when training machine learning models, there is little theoretical understanding on the generalization error of such methods. In this work, we first show that th ...

The rise of robotic body augmentation brings forth new developments that will transform robotics, human-machine interaction, and wearable electronics. Extra robotic limbs, although building upon restorative technologies, bring their own set of challenges i ...

Hyperbolic lattices are a new type of synthetic materials based on regular tessellations in non-Euclidean spaces with constant negative curvature. While so far, there has been several theoretical investigations of hyperbolic topological media, experimental ...

Modular robotics link the reliability of a centralised system with the adaptivity of a decentralised system. It is difficult for a robot with a fixed shape to be able to perform many different types of tasks. As the task space grows, the number of functi ...

Controlling complex tasks in robotic systems, such as circular motion for cleaning or following curvy lines, can be dealt with using nonlinear vector fields. This article introduces a novel approach called the rotational obstacle avoidance method (ROAM) fo ...

In light of the challenges posed by climate change and the goals of the Paris Agreement, electricity generation is shifting to a more renewable and decentralized pattern, while the operation of systems like buildings is increasingly electrified. This calls ...

Modern optimization is tasked with handling applications of increasingly large scale, chiefly due to the massive amounts of widely available data and the ever-growing reach of Machine Learning. Consequently, this area of research is under steady pressure t ...

Soft robot are celebrated for their propensity to enable compliant and complex robot-environment interactions. Soft robotic manipulators, or slender continuum structure robots have the potential to exploit these interactions to enable new exploration and m ...

Dynamical System (DS)-based closed-loop control is a simple and effective way to generate reactive motion policies that well generalize to the robotic workspace, while retaining stability guarantees. Lately the formalism has been expanded in order to handl ...

Model-based reinforcement learning for robot control offers the advantages of overcoming concerns on data collection and iterative processes for policy improvement in model-free methods. However, both methods use exploration strategy relying on heuristics ...

We develop a principled approach to end-to-end learning in stochastic optimization. First, we show that the standard end-to-end learning algorithm admits a Bayesian interpretation and trains a posterior Bayes action map. Building on the insights of this an ...

Programming intelligent robots requires robust controllers that can achieve desired tasks while adapting to the changes in the task and the environment. In this thesis, we address the challenges in designing such adaptive and anticipatory feedback controll ...

Novel technologies, fabrication methods, controllers and computational methods are rapidly advancing the capabilities of soft robotics. This is creating the need for design techniques and methodologies that are suited for the multi-disciplinary nature of s ...

Soft robots aim to revolutionize how robotic systems interact with the environment thanks to their inherent compliance. Some of these systems are even able to modulate their physical softness. However, simply equipping a robot with softness will not genera ...

Learning by Imitation with the STIFF-FLOP Surgical Robot: A Biomimetic Approach Inspired by Octopus Movements

Chattez avec Graph Search

Robot Learning using Tensor Networks

Reactive collision-free motion generation in joint space via dynamical systems and sampling-based MPC

Geometric Learning: Leveraging differential geometry for learning and control

Exact Obstacle Avoidance for Robots in Complex and Dynamic Environments Using Local Modulation

Filiform Variable Stiffness Technologies for Medical Robotics

Violation-aware contextual Bayesian optimization for controller performance optimization with unmodeled constraints

On the Generalization of Stochastic Gradient Descent with Momentum

Investigating neural resource allocation in the sensorimotor control of extra limbs

Anomalous and Chern topological waves in hyperbolic networks

Memento Mori: Reliable robustness in self-reconfigurable modular robots

Avoidance of Concave Obstacles Through Rotation of Nonlinear Dynamics

Quantifying the Unknown: Data-Driven Approaches and Applications in Energy Systems

Scalable constrained optimization

Piecewise Affine Curvature model: a reduced-order model for soft robot-environment interaction beyond PCC

Hybrid Quadratic Programming - Pullback Bundle Dynamical Systems Control

Exploration-based model learning with self-attention for risk-sensitive robot control

End-to-End Learning for Stochastic Optimization: A Bayesian Perspective

Learning and optimization of anticipatory feedback controllers for robot manipulation

The science of soft robot design: A review of motivations, methods and enabling technologies

Prescribing Cartesian Stiffness of Soft Robots by Co-Optimization of Shape and Segment-Level Stiffness