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Joni Dambre

7 accepted papers

2020

Stance Control Inspired by Cerebellum Stabilizes Reflex-Based Locomotion on HyQ Robot

ICRA 2020poster

Advances in legged robotics are strongly rooted in animal observations. A clear illustration of this claim is the generalization of Central Pattern Generators (CPG), first identified in the cat spinal cord, to generate cyclic motion in robotic locomotion. Despite a global endorsement of this model,…

Cited by 9SourceScholar
2018

BRUNO: A Deep Recurrent Model for Exchangeable Data

NeurIPS 2018poster

We present a novel model architecture which leverages deep learning tools to perform exact Bayesian inference on sets of high dimensional, complex observations. Our model is provably exchangeable, meaning that the joint distribution over observations is invariant under permutation: this property lie…

2017

A Differentiable Physics Engine for Deep Learning in Robotics

ICLR 2017workshop

One of the most important fields in robotics is the optimization of controllers. Currently, robots are often treated as a black box in this optimization process, which is the reason why derivative-free optimization methods such as evolutionary algorithms or reinforcement learning are omnipresent. Wh…

Cited by 274SourceScholar
2015

Developing an embodied gait on a compliant quadrupedal robot

IROS 2015poster

Incorporating the body dynamics of compliant robots into their controller architectures can drastically reduce the complexity of locomotion control. An extreme version of this embodied control principle was demonstrated in highly compliant tensegrity robots, for which stable gait generation was achi…

Cited by 35SourceScholar
2015

Online unsupervised terrain classification for a compliant tensegrity robot using a mixture of echo state networks

ICRA 2015poster

Truly autonomous robots require the capacity to recognise their surroundings by interpreting their sensorimotor stream. We present an online learning algorithm for training a mixture of echo state network experts that can segment a compliant robot's sensorimotor stream. Our method follows a probabil…

Cited by 7SourceScholar