Learning How to Walk: Warm-starting Optimal Control Solver with Memory of Motion

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Publications associées (31)

Sensorless force and displacement estimation in soft actuators

Jamie Paik, Sagar Dattatray Joshi

Sensing forms an integral part of soft matter based robots due to their compliance, dependence on loading conditions, and virtually infinite degrees of freedom. Previous studies have developed several extrinsic sensors and embedded them into soft actuators ...

ROYAL SOC CHEMISTRY2023

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

Ultra-tunable bistable structures for universal robotic applications

Jamie Paik, Ke Liu, Xin Tong, Lei Wang, Hongying Zhang, Yongkang Jiang

Bistable structures in nature are unparalleled for their fast response and force amplification even with the most minute physical stimula-tion. However, current works on bistable structures mainly focus on their stable states, while promising intermediate ...

CELL PRESS2023

The neuromechanics of animal locomotion: From biology to robotics and back

Auke Ijspeert, Pavan P Ramdya

Robotics and neuroscience are sister disciplines that both aim to understand how agile, efficient, and robust locomotion can be achieved in autonomous agents. Robotics has already benefitted from neuromechanical principles discovered by investigating anima ...

AMER ASSOC ADVANCEMENT SCIENCE2023

Bayes-optimal Learning of Deep Random Networks of Extensive-width

Hugo Chao Cui, Florent Gérard Krzakala, Lenka Zdeborová

We consider the problem of learning a target function corresponding to a deep, extensive-width, non-linear neural network with random Gaussian weights. We consider the asymptotic limit where the number of samples, the input dimension and the network width ...

2023

DARE-GRAM : Unsupervised Domain Adaptation Regression by Aligning Inverse Gram Matrices

Ismail Nejjar, Olga Fink

Unsupervised Domain Adaptation Regression (DAR) aims to bridge the domain gap between a labeled source dataset and an unlabelled target dataset for regression problems. Recent works mostly focus on learning a deep feature encoder by minimizing the discrepa ...

IEEE2023

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

Beyond undulation! Body morphology and sensing components of elongated animals and robots reveal skills to maintain competent locomotion

Laura Isabel Paez Coy

Locomotion is an essential evolutive innovation of living beings that allows them to colonize and dominate the planet. As diverse as animal morphologies are (living) and were (extinct), their locomotion modalities are also diverse. In particular, animal mo ...

EPFL2023

Inferring individual evaluation criteria for reaching trajectories with obstacle avoidance from EEG signals

Aude Billard, José del Rocio Millán Ruiz, Fumiaki Iwane

During reaching actions, the human central nerve system (CNS) generates the trajectories that optimize effort and time. When there is an obstacle in the path, we make sure that our arm passes the obstacle with a sufficient margin. This comfort margin varie ...

Nature Portfolio2023

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

Josephine Anna Eleanor Hughes, Francesco Stella

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 ...

MARY ANN LIEBERT, INC2023

Identification of the trade-off between speed and efficiency in undulatory swimming using a bio-inspired robot

Auke Ijspeert, Laura Isabel Paez Coy, Kamilo Andres Melo Becerra, Karen Ann J Mulleners, Alexandros Anastasiadis, Eric Daniel Tytell

Anguilliform swimmers, like eels or lampreys, are highly efficient swimmers. Key to understanding their performances is the relationship between the body’s kinematics and resulting swimming speed and efficiency. But, we cannot prescribe kinematics to livin ...

Nature Research2023

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