A Control and Optimization Framework for Robot Locomotion : from Model-free to Model-based Approaches

À propos
Confidentialité
Mentions légales

Graph Chatbot

Publications associées (30)

An Optimal Control Formulation of Tool Affordance Applied to Impact Tasks

Sylvain Calinon, Jie Zhao

Humans use tools to complete impact-aware tasks, such as hammering a nail or playing tennis. The postures adopted to use these tools can significantly influence the performance of these tasks, where the force or velocity of the hand holding a tool plays a ...

Ieee-Inst Electrical Electronics Engineers Inc2024

Carbon based printed electrodes for DEAs: study of pad, inkjet, and stencil printing

Yves Perriard, Yoan René Cyrille Civet, Thomas Guillaume Martinez, Stefania Maria Aliki Konstantinidi, Armando Matthieu Walter, Simon Holzer

Dielectric elastomer actuators (DEAs) have raised interest due to their remarkable capabilities in various applications, such as soft robotics, haptic feedback systems, and biomedical devices. To harness the full potential of DEAs, the choice of the electr ...

Society of Photo-Optical Instrumentation Engineers (SPIE)2024

Permanently magnetized elastomer rotating actuator using traveling waves

Herbert Shea

We report a soft actuator that generates continuous rotation of an object placed on it by electromagnetically exciting circular travelling waves in a soft disk. The disk, that serves as the stator, is made of a stretchable composite consisting of segments ...

Iop Publishing Ltd2024

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

Sudong Lee

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 ...

2023

Morphological flexibility in robotic systems through physical polygon meshing

Jamie Paik, Christoph Heinrich Belke, Kevin Andrew Holdcroft, Alexander Thomas Sigrist

Shape-changing robots adapt their own morphology to address a wider range of functions or environments than is possible with a fixed or rigid structure. Akin to biological organisms, the ability to alter shape or configuration emerges from the underlying m ...

Nature Portfolio2023

Self-Correcting Quadratic Programming-Based Robot Control

Aude Billard, Konstantinos Chatzilygeroudis, Farshad Khadivar

Quadratic Programming (QP)-based controllers allow many robotic systems, such as humanoids, to successfully undertake complex motions and interactions. However, these approaches rely heavily on adequately capturing the underlying model of the environment a ...

2023

Neuromechanical Simulations of Animats with Decentralised Control

Jonathan Patrick Arreguit O'Neill

The transition from aquatic to terrestrial environments represents a significant event in the history of evolution. For this transition to occur, animals had to adapt their morphology, physiology and locomotory skills to handle the challenges and interacti ...

EPFL2023

Human-Robot Swarm Interaction: An Explorative Path to Foster Complex Systems Understanding

Hala Khodr

Order, regularities, and patterns are ubiquitous around us. A flock of birds maneuvering in the sky, the self-organization of social insects, a global pandemic or a traffic jam are examples of complex systems where the macroscopic patterns arise from the m ...

EPFL2023

Puppeteer and Marionette: Learning Anticipatory Quadrupedal Locomotion Based on Interactions of a Central Pattern Generator and Supraspinal Drive

Auke Ijspeert, Milad Shafiee Ashtiani, Guillaume Denis Antoine Bellegarda

Quadruped animal locomotion emerges from the interactions between the spinal central pattern generator (CPG), sensory feedback, and supraspinal drive signals from the brain. Computational models of CPGs have been widely used for investigating the spinal co ...

IEEE2023

Identifying important sensory feedback for learning locomotion skills

Auke Ijspeert, Milad Shafiee Ashtiani, Guillaume Denis Antoine Bellegarda

Robot motor skills can be acquired by deep reinforcement learning as neural networks to reflect state-action mapping. The selection of states has been demonstrated to be crucial for successful robot motor learning. However, because of the complexity of neu ...

NATURE PORTFOLIO2023

Page 2 sur 2